##           used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells  534135 28.6    1191394 63.7         NA   669411 35.8
## Vcells 1005212  7.7    8388608 64.0     131072  1851587 14.2

0.1 Note

Here we the ABCD release 4.0 data-set

This file has all the linear mixed models with genes predictions as a variable. The reasons in doing this is that the number of observations in the genes data is substantially less than all the other variables. To be more specific: we have around 5,000 observations in genes data and 11,000 observations in mental health, social demographic lifestyle developmental and sets of brain features.

1 Setting up the environments

1.1 Loading libraries

The following libraries and default settings were used during the analysis:

options(scipen = 999)
#library("sva")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
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library("tidymodels")
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## ✔ broom        1.0.5     ✔ rsample      1.1.1
## ✔ dials        1.2.0     ✔ tune         1.1.1
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## ✔ modeldata    1.2.0     ✔ workflowsets 1.0.1
## ✔ parsnip      1.1.0     ✔ yardstick    1.2.0
## ✔ recipes      1.0.7     
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## ✖ yardstick::spec() masks readr::spec()
## ✖ recipes::step()   masks stats::step()
## • Dig deeper into tidy modeling with R at https://www.tmwr.org
##parallel map
library(partR2)
## 
## Attaching package: 'partR2'
## 
## The following object is masked from 'package:modeldata':
## 
##     biomass
library(lme4)
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## 
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## 
##     expand, pack, unpack
library(yhat)
library(ggtext)
library(ggpubr)
library(cowplot)
## 
## Attaching package: 'cowplot'
## 
## The following object is masked from 'package:ggpubr':
## 
##     get_legend
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library(partR2)
library(lme4)
library(yhat)
library("VennDiagram")
## Loading required package: grid
## Loading required package: futile.logger
## 
## Attaching package: 'VennDiagram'
## 
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## library to tidy up the linear mixed models
library(sjPlot)
## 
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##     plot_grid, save_plot
theme_set(theme_bw() + theme(panel.grid = element_blank()))
## parallel processing number of cores register
all_cores <- parallel::detectCores(logical = FALSE) - 5

doParallel::registerDoParallel(cores = all_cores)

1.2 Setting up the path

We first loaded all of the relevant data files (not shown here as they refer to local directories):

2 Data Preparation

2.1 load fitted gfactors from brain scan and psychopathology

2.1.1 load brain scan fitted results from stacking models

rf_baseline <- readRDS(paste0(scriptfold,"stacking_gfactor_modelling/collect_random_forest_results/random_forest_baseline_results.RDS"))

rf_followup <- readRDS(paste0(scriptfold,"stacking_gfactor_modelling/collect_random_forest_results/random_forest_followup_results.RDS"))

2.1.2 load the mental health predicted results

psy_pred <- readRDS(paste0(scriptfold,"Common_psy_gene_brain_all/saved_outputs/psychopathology_pls_pred_2.0.RData"))

2.1.3 load the social demographic lifestyle developmental predicted results

ses_pred <- readRDS(paste0(scriptfold,"genetics_psychopathology_common_scan_all_scripts/ses_pls_pred.RData"))

2.1.5 load the computed gfactor

gfactor_list <- readRDS(paste0(scriptfold,"genetics_psychopathology_common_scan_all_scripts/gfactor_scale_seperate.RData"))

2.2 process loaded brain, genes, mental health and social demographic lifestyle developmental model predictions

Be careful with what site you choose.

rf_baseline_pred <- map(rf_baseline,"test_pred")
rf_baseline_pred_tibble <- map(rf_baseline_pred,"model_predict")
rf_baseline_table <- map(rf_baseline,"test_data")

rf_followup_pred <- map(rf_followup,"test_pred")
rf_followup_pred_tibble <- map(rf_followup_pred,"model_predict")
rf_followup_table <- map(rf_followup,"test_data")
gfactor_baselie_test <- map(gfactor_list,"output_test_baseline")
gfactor_followup_test <- map(gfactor_list,"output_test_followup")
subj_info <- c("SUBJECTKEY","SITE_ID_L","EVENTNAME")

Functions to process the loaded prediction results.

pred_processing <- function(baseline_pred, followup_pred, baseline_table, followup_table,pred_name){
  names_vec <- c(subj_info,pred_name)
  
  baseline_subj_info <- baseline_table %>% select(all_of(subj_info))
  baseline_pred_vec <- baseline_pred %>% select(model_predict)
  baseline_output <- bind_cols(baseline_subj_info,baseline_pred_vec)
  names(baseline_output) <- names_vec
  
  followup_subj_info <- followup_table %>% select(all_of(subj_info))
  followup_pred_vec <- followup_pred %>% select(model_predict)
  followup_output <- bind_cols(followup_subj_info,followup_pred_vec)
  names(followup_output) <- names_vec

  output_all <- bind_rows(baseline_output,followup_output)
  return(list(baseline_output = baseline_output,
              followup_output = followup_output))
}

Combine all the subject information and prediction together and change the names.

processed_rf_results <- pmap(list(rf_baseline_pred_tibble,rf_baseline_table,
                                  rf_followup_pred_tibble,rf_followup_table),
                             ~pred_processing(baseline_pred=..1,
                                              followup_pred=..3, 
                                              baseline_table=..2, 
                                              followup_table=..4,
                                              pred_name="random_forest_stacking"))


processed_psy_results <- pmap(list(psy_pred[["baseline_test_pred"]],
                                   psy_pred[["baseline_test_data"]],
                                  psy_pred[["followup_test_pred"]],
                                  psy_pred[["followup_test_data"]]),
                             ~pred_processing(baseline_pred=..1,
                                              followup_pred=..3, 
                                              baseline_table=..2, 
                                              followup_table=..4,
                                              pred_name="psychopathology_pls"))


processed_ses_results <- pmap(list(ses_pred[["baseline_test_pred"]],
                                   ses_pred[["baseline_test_data"]],
                                  ses_pred[["followup_test_pred"]],
                                  ses_pred[["followup_test_data"]]),
                             ~pred_processing(baseline_pred=..1,
                                              followup_pred=..3, 
                                              baseline_table=..2, 
                                              followup_table=..4,
                                              pred_name="ses_pls"))
site_char <- names(processed_psy_results)

2.2.1 prepocessing genetics prediction results

load in family index data

ACS <-read_csv(paste0(datafolder,"ACSPSW03_DATA_TABLE.csv")) 
## Rows: 23101 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): SUBJECTKEY, SRC_SUBJECT_ID, INTERVIEW_DATE, SEX, EVENTNAME, GENETI...
## dbl (18): ACSPSW03_ID, DATASET_ID, INTERVIEW_AGE, RACE_ETHNICITY, REL_FAMILY...
## lgl  (6): GENETIC_PAIRED_SUBJECTID_4, GENETIC_PI_HAT_3, GENETIC_PI_HAT_4, GE...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#knitr::kable(glimpse(ACS))

#race_ethnicity
#1 = White; 2 = Black; 3 = Hispanic; 4 = Asian; 5 = Other

# guardian-report relationship
# Relationship of the participant in his or her family
# 0 = single; 1 = sibling; 2 = twin; 3 = triplet
# ACS %>% count(REL_RELATIONSHIP)

ACSselected <- ACS %>% 
  select(SUBJECTKEY, EVENTNAME, SEX, INTERVIEW_AGE, RACE_ETHNICITY, 
                              REL_FAMILY_ID, ACS_RAKED_PROPENSITY_SCORE) %>%
  mutate(RACE_ETHNICITY = recode_factor(as.factor(RACE_ETHNICITY),
                `1` = "White", `2` = "Black", `3` = "Hispanic", `4` = "Asian", `5` = "Other",
                .default = "White")) %>%
  mutate(SEX = as.factor(SEX)) %>%
  mutate(REL_FAMILY_ID = as.factor(REL_FAMILY_ID))

ACSselected %>%
 filter(EVENTNAME =="baseline_year_1_arm_1") %>%
 skimr::skim()
Data summary
Name Piped data
Number of rows 11876
Number of columns 7
_______________________
Column type frequency:
character 2
factor 3
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
SUBJECTKEY 0 1 12 16 0 11876 0
EVENTNAME 0 1 21 21 0 1 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
SEX 0 1 FALSE 2 M: 6196, F: 5680
RACE_ETHNICITY 2 1 FALSE 5 Whi: 6180, His: 2411, Bla: 1784, Oth: 1247
REL_FAMILY_ID 0 1 FALSE 9854 373: 5, 749: 4, 11: 3, 400: 3

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
INTERVIEW_AGE 0 1 118.98 7.50 107.00 112.00 119.00 126.00 133.00 ▇▆▆▆▆
ACS_RAKED_PROPENSITY_SCORE 0 1 691.34 350.98 161.36 449.35 619.31 821.74 1778.92 ▅▇▂▂▁ 
ACSselected_baseline <- ACSselected %>%
                        filter(EVENTNAME =="baseline_year_1_arm_1")%>%
                        select(SUBJECTKEY,REL_FAMILY_ID)

ACSselected_followup <- ACSselected %>%
                        filter(EVENTNAME =="1_year_follow_up_y_arm_1")%>%
                        select(SUBJECTKEY,REL_FAMILY_ID)

Process the genes data.

genetics_cog_baseline_pred <-genetics_cog[["baseline_test_pred"]]
genetics_cog_baseline_table <-genetics_cog[["baseline_test_data"]]

genetics_cog_followup_pred <- genetics_cog[["followup_test_pred"]]
genetics_cog_followup_data <- genetics_cog[["followup_test_data"]]


pred_processing_gene <- function(baseline_pred, followup_pred, baseline_table, followup_table,pred_name){
  names_vec <- c(c("SUBJECTKEY","SITE_ID_L"),pred_name)
  
  baseline_subj_info <- baseline_table %>% select(all_of(c("SUBJECTKEY","SITE_ID_L")))
  baseline_pred_vec <- baseline_pred %>% select(model_predict)
  baseline_output <- bind_cols(baseline_subj_info,baseline_pred_vec)
  names(baseline_output) <- names_vec
  
  followup_subj_info <- followup_table %>% select(all_of(c("SUBJECTKEY","SITE_ID_L")))
  followup_pred_vec <- followup_pred %>% select(model_predict)
  followup_output <- bind_cols(followup_subj_info,followup_pred_vec)
  names(followup_output) <- names_vec

  output_all <- bind_rows(baseline_output,followup_output)
  return(list(baseline_output = baseline_output,
              followup_output = followup_output))
}

processed_gene_results <- pmap(list(genetics_cog_baseline_pred,genetics_cog_baseline_table,
                                  genetics_cog_followup_pred,genetics_cog_followup_data),
                             ~pred_processing_gene(baseline_pred=..1,
                                              followup_pred=..3, 
                                              baseline_table=..2, 
                                              followup_table=..4,
                                              pred_name="gene_cog"))

count the total number in the genetics

genetics_cog_baseline_tibble <- map(genetics_cog_baseline_table,~dim(.))%>%
  do.call(rbind,.)

sum(genetics_cog_baseline_tibble[,1])
## [1] 5786
### checking unique IDs

genetics_cog_baseline_ID <- map(genetics_cog_baseline_table,~unique(.[["SUBJECTKEY"]])%>%length())%>%
                            do.call(rbind,.)
sum(genetics_cog_baseline_ID)
## [1] 5786

Join the brain, genes, mental health and social demographic lifestyle developmental model predictions

join_data_all <- function(site_input){
  ### join the data and drop NA
  rf_baseline_tibble <- processed_rf_results[[site_input]][["baseline_output"]]
  rf_followup_tibble <- processed_rf_results[[site_input]][["followup_output"]]
  psy_baseline_tibble <- processed_psy_results[[site_input]][["baseline_output"]]
  psy_followup_tibble <- processed_psy_results[[site_input]][["followup_output"]]
  ses_baseline_tibble <- processed_ses_results[[site_input]][["baseline_output"]]
  ses_followup_tibble <- processed_ses_results[[site_input]][["followup_output"]]
  

  baseline_rf_psy_ses <- plyr::join_all(list(rf_baseline_tibble,
                                      psy_baseline_tibble,
                                      ses_baseline_tibble), 
                                 by =subj_info, type = 'full')
  followup_rf_psy_ses <- plyr::join_all(list(rf_followup_tibble,
                                      psy_followup_tibble,
                                      ses_followup_tibble), 
                                 by =subj_info, type = 'full')
  


   gene_baseline_tibble <-processed_gene_results[[site_input]][["baseline_output"]]
  gene_followup_tibble <- processed_gene_results[[site_input]][["followup_output"]]
  
  
   baseline_rf_psy_ses_gene <- full_join(baseline_rf_psy_ses,
                                      gene_baseline_tibble, 
                                 by =c("SUBJECTKEY","SITE_ID_L" ))
  followup_rf_psy_ses_gene <- full_join(followup_rf_psy_ses,
                                      gene_followup_tibble, 
                                 by =c("SUBJECTKEY","SITE_ID_L" ))
  

  gfactor_baseline <- gfactor_baselie_test[[site_input]]
  gfactor_followup <- gfactor_followup_test[[site_input]]
  
  baseline_all <- plyr::join_all(list(baseline_rf_psy_ses_gene,
                                      ACSselected_baseline,gfactor_baseline), 
                                 by ="SUBJECTKEY", type = 'full')%>%
                                 drop_na()
  followup_all <- plyr::join_all(list(followup_rf_psy_ses_gene,
                                      ACSselected_baseline,gfactor_followup), 
                                 by ="SUBJECTKEY", type = 'full')%>%
                                 drop_na()

 output_all <- bind_rows(baseline_all,followup_all)
  
  return(list(baseline = baseline_all,
              followup=followup_all,
              all = output_all))
  
}
gfactor_all <- map(.x = site_char,~join_data_all(site_input = .x))

names(gfactor_all) <- site_char

gfactor_all_baseline <- map(gfactor_all,"baseline")
gfactor_all_followup <- map(gfactor_all,"followup")
gfactor_all_baseline_followup <- map(gfactor_all,"all")

2.3 Mean centering within family and within site

This script only perform the commonality analyses for the models with genetics. In genetics there are only around 5000 participants, which is much smaller than all the other models.

Some explanation of the variables:

brain_savg: the average of brain prediction results within each site.

brain_cws: brain predictions centered within each site.

mental_savg: the average of mental health predictions results within each site.

mental_cws: mental health predictions centered within each site.

sdl_savg: the average of social demographic lifestyle predictions results within each site.

sdl_cws: social demographic lifestyle predictions centered within each site.

gene_favg: the average of gene predictions results within each family.

gene_cwf: gene predictions centered within each family (basically zeros as the data is comprised of single child in those families).

gene_savg_favg: averaged gene_favg estimates across each site

gene_cws_cwf: gene_favg estimates centered within each site.

center_by_family_site <- function(data_input){
  
 data_input <- data_input[order(data_input$REL_FAMILY_ID),]
 count_by_family <- data_input %>% count(REL_FAMILY_ID)

 
 mean_by_family_gene <- data_input %>% group_by(REL_FAMILY_ID)%>%
                                 summarise_at(vars(gene_cog),mean)
 
 mean_vec_gene <- rep(mean_by_family_gene$gene_cog,times = count_by_family$n)
 
 data_gene  <- data_input%>%
                 mutate(gene_favg = mean_vec_gene,
                        gene_cwf = gene_cog - gene_favg)
 
 
 output_data <- data_gene %>% 
    mutate(brain_savg = mean(random_forest_stacking))%>% 
    mutate(brain_cws = random_forest_stacking-brain_savg)%>% 
   mutate(mental_savg = mean(psychopathology_pls))%>%
    mutate(mental_cws = psychopathology_pls - mental_savg)%>% 
    mutate(gene_savg_favg = mean(gene_favg))%>%
    mutate(gene_cws_cwf = gene_cwf - gene_savg_favg)%>% 
   mutate(sdl_savg = mean(ses_pls))%>%
    mutate(sdl_cws = ses_pls - sdl_savg)

return(output_data)
  }


centered_all_baseline <- map(gfactor_all_baseline,~center_by_family_site(data_input = .))
centered_all_followup <- map(gfactor_all_followup,~center_by_family_site(data_input = .))

2.4 Scale the overall data by each variable

gfactor_all_pred_centered_site_baseline <- centered_all_baseline%>%
                                           do.call(rbind,.)
gfactor_all_pred_centered_site_followup <- centered_all_followup%>%
                                           do.call(rbind,.)


features_not_scale <- c(subj_info, "REL_FAMILY_ID","gfactor")

features_scale <- gfactor_all_pred_centered_site_baseline %>% select(-all_of(features_not_scale))%>% colnames()
## recipe to scale the features
recipe_scale <- function(train_input=gfactor_all_pred_centered_site_baseline){
  norm_recipe <- recipe( as.formula("gfactor~."), data = train_input) %>%
    update_role(all_of(features_scale), new_role = "predictor")%>%
    update_role("gfactor", new_role = "outcome" )%>%
    # normalize numeric predictors and outcome
    step_normalize(all_numeric_predictors())
  return(norm_recipe)
}


all_baseline_recipe <- 

data_all_baseline <-recipe_scale(train_input=gfactor_all_pred_centered_site_baseline) %>%
                    prep() %>%
                    juice()


data_all_followup <-recipe_scale(train_input=gfactor_all_pred_centered_site_followup) %>%
                    prep() %>%
                    juice()

3 commonality analysis between stacked brain scan, psychopathology and genetics that predicts cognition

Commonality analysis with the linear mixed models

Commonality between three features within a 4 level nested structure

Measurements are nested within subjects

Subjects are nested within families

Families are nested within sites

Due to the age requirements of all the subjects. Participants within the family are likely to be twins.

Because the data structure (number of subjects) and the modelling methods (through grid search) are so different between baseline and followup. The analysis is done separately between them.

Note: this model is not included in the finally manuscript.

3.1 Function to compute the commonality coefficients

common_analysis_gene_psy_brain_multi_both <- function(data_input=gfactor_all_pred_centered_site_all){
print("full_model")  
  full_model <- lmer(gfactor ~ brain_savg+ brain_cws+ mental_savg+mental_cws+gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_full_model <- performance::r2(full_model)
  
    print("brain_psy_model") 
  brain_psy_model <- lmer(gfactor ~ brain_savg+ brain_cws+ mental_cws+mental_savg+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_brain_psy <- performance::r2(brain_psy_model)
 

  print("gene_psy_model")
  ## this model has singularity problem
  gene_psy_model <- lmer(gfactor ~mental_cws+mental_savg+gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_gene_psy <- performance::r2(gene_psy_model)


   print("brain_gene_model") 
  brain_gene_model <- lmer(gfactor ~ brain_savg+ brain_cws+gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_brain_gene <- performance::r2(brain_gene_model)


  print("brain_model")
  brain_model <- lmer(gfactor ~ brain_savg+ brain_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_brain <- performance::r2(brain_model)


  print("psy_model")
  psy_model <- lmer(gfactor ~  mental_cws+mental_savg+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_psy <- performance::r2(psy_model)

    print("gene_model")
  gene_model <- lmer(gfactor ~ gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsp_gene <- performance::r2(gene_model)

  
  unique_gene_margin <- rsq_full_model$R2_marginal-rsq_brain_psy$R2_marginal
    
  unique_brain_margin <- rsq_full_model$R2_marginal-rsq_gene_psy$R2_marginal
  
  unique_psy_margin <- rsq_full_model$R2_marginal-rsq_brain_gene$R2_marginal
    
  common_brain_psy_margin <- rsq_brain_gene$R2_marginal+rsq_gene_psy$R2_marginal-rsp_gene$R2_marginal-rsq_full_model$R2_marginal
  
  common_gene_psy_margin <- rsq_brain_gene$R2_marginal+rsq_brain_psy$R2_marginal-rsq_brain$R2_marginal-rsq_full_model$R2_marginal
  
  common_brain_gene_margin <- rsq_brain_psy$R2_marginal+rsq_gene_psy$R2_marginal-rsq_psy$R2_marginal-rsq_full_model$R2_marginal
  
  common_brain_gene_psy_margin <-rsp_gene$R2_marginal+rsq_brain$R2_marginal+rsq_psy$R2_marginal-rsq_brain_gene$R2_marginal-rsq_gene_psy$R2_marginal-rsq_brain_psy$R2_marginal+rsq_full_model$R2_marginal
  
  
  
  unique_gene_conditional <- rsq_full_model$R2_conditional-rsq_brain_psy$R2_conditional
    
  unique_brain_conditional <- rsq_full_model$R2_conditional-rsq_gene_psy$R2_conditional
  
  unique_psy_conditional <- rsq_full_model$R2_conditional-rsq_brain_gene$R2_conditional
    
  common_brain_psy_conditional <- rsq_brain_gene$R2_conditional+rsq_gene_psy$R2_conditional-rsp_gene$R2_conditional-rsq_full_model$R2_conditional
  
  common_gene_psy_conditional <- rsq_brain_gene$R2_conditional+rsq_brain_psy$R2_conditional-rsq_brain$R2_conditional-rsq_full_model$R2_conditional
  
  common_brain_gene_conditional <- rsq_brain_psy$R2_conditional+rsq_gene_psy$R2_conditional-rsq_psy$R2_conditional-rsq_full_model$R2_conditional
  
  common_brain_gene_psy_conditional <-rsp_gene$R2_conditional+rsq_brain$R2_conditional+rsq_psy$R2_conditional-rsq_brain_gene$R2_conditional-rsq_gene_psy$R2_conditional-rsq_brain_psy$R2_conditional+rsq_full_model$R2_conditional
  
  
  output_common_tibble <- tibble(variable_effects = c("unique_gene",
                                               "unique_brain",
                                              "unique_psy",
                                              "common_brain_psy",
                                              "common_gene_psy",
                                              "common_brain_gene",
                                              "common_brain_gene_psy"),
                          marginal_rsq = c(unique_gene_margin,
                                              unique_brain_margin,
                                              unique_psy_margin,
                                              common_brain_psy_margin,
                                              common_gene_psy_margin,
                                              common_brain_gene_margin,
                                              common_brain_gene_psy_margin),
                          conditional_rsq = c(unique_gene_conditional,
                                              unique_brain_conditional,
                                              unique_psy_conditional,
                                              common_brain_psy_conditional,
                                              common_gene_psy_conditional,
                                              common_brain_gene_conditional,
                                              common_brain_gene_psy_conditional))
  output_rsq_tibble <- tibble(model_names<- c("gene",
                                               "brain",
                                              "psy",
                                              "brain_psy",
                                              "gene_psy",
                                              "brain_gene",
                                              "brain_gene_psy"),
                          marginal_rsq = c(rsp_gene$R2_marginal,
                                              rsq_brain$R2_marginal,
                                              rsq_psy$R2_marginal,
                                              rsq_brain_psy$R2_marginal,
                                              rsq_gene_psy$R2_marginal,
                                              rsq_brain_gene$R2_marginal,
                                              rsq_full_model$R2_marginal),
                          conditional_rsq = c(rsp_gene$R2_conditional,
                                              rsq_brain$R2_conditional,
                                              rsq_psy$R2_conditional,
                                              rsq_brain_psy$R2_conditional,
                                              rsq_gene_psy$R2_conditional,
                                              rsq_brain_gene$R2_conditional,
                                              rsq_full_model$R2_conditional))
  
  return(list(output_common_tibble=output_common_tibble,
              output_rsq_tibble=output_rsq_tibble,
              full_model=full_model,
              brain_psy_model=brain_psy_model,
              gene_psy_model=gene_psy_model,
              brain_gene_model=brain_gene_model,
             brain_model=brain_model,
             psy_model=psy_model,
             gene_model=gene_model
             ))
  }

3.2 Process the table results

common_analysis_gene_psy_brain_all_site_both <-common_analysis_gene_psy_brain_multi_both(data_input=data_all_baseline)
## [1] "full_model"
## [1] "brain_psy_model"
## [1] "gene_psy_model"
## [1] "brain_gene_model"
## [1] "brain_model"
## [1] "psy_model"
## [1] "gene_model"
common_analysis_gene_psy_brain_all_site_both_followup <-common_analysis_gene_psy_brain_multi_both(data_input=data_all_followup)
## [1] "full_model"
## [1] "brain_psy_model"
## [1] "gene_psy_model"
## [1] "brain_gene_model"
## [1] "brain_model"
## [1] "psy_model"
## [1] "gene_model"
baseline_plot_vec <- common_analysis_gene_psy_brain_all_site_both[[1]]$marginal_rsq
#baseline_plot_vec_corrected <- baseline_plot_vec-baseline_plot_vec[5]/6 
#### get rid of the negative values
#baseline_plot_vec_corrected[5] <- 0


baseline_plot_vec_percent <- baseline_plot_vec/sum(baseline_plot_vec)*100 
baseline_plot_vec_percent <- round(baseline_plot_vec_percent,2)
print(baseline_plot_vec_percent)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        1.39       54.12       15.93       20.31        1.74        0.24 
## Marginal R2 
##        6.28
baseline_plot_vec_raw <- baseline_plot_vec*100 
baseline_plot_vec_raw <- round(baseline_plot_vec_raw,2)
print(baseline_plot_vec_raw)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        0.36       13.91        4.09        5.22        0.45        0.06 
## Marginal R2 
##        1.61
followup_plot_vec <- common_analysis_gene_psy_brain_all_site_both_followup[[1]]$marginal_rsq
#baseline_plot_vec_corrected_followup <- baseline_plot_vec_followup-baseline_plot_vec_followup[5]/6
#### get rid of the negative values
#baseline_plot_vec_corrected_followup[5] <- 0


followup_plot_vec_percentage <- followup_plot_vec/sum(followup_plot_vec)*100
followup_plot_vec_percentage <- round(followup_plot_vec_percentage,2)
print(followup_plot_vec_percentage)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        0.49       59.46       17.70       19.82        0.23        0.83 
## Marginal R2 
##        1.47
followup_plot_vec_raw <- followup_plot_vec*100 
followup_plot_vec_raw <- round(followup_plot_vec_raw,2)
print(followup_plot_vec_raw)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        0.11       12.97        3.86        4.32        0.05        0.18 
## Marginal R2 
##        0.32
baseline_gene_psy_brain_table <- common_analysis_gene_psy_brain_all_site_both[[1]]%>%
                                 mutate(corrected_percent = baseline_plot_vec_percent)%>%
                                 mutate(corrected_raw = baseline_plot_vec_raw)




baseline_gene_psy_brain_table%>% 
    kableExtra::kbl(caption = "Commonality analysis in baseline") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
Commonality analysis in baseline
variable_effects marginal_rsq conditional_rsq corrected_percent corrected_raw
unique_gene 0.0035603 0.0071279 1.39 0.36
unique_brain 0.1390674 0.0363902 54.12 13.91
unique_psy 0.0409285 0.0292855 15.93 4.09
common_brain_psy 0.0521786 0.0100158 20.31 5.22
common_gene_psy 0.0044788 -0.0002736 1.74 0.45
common_brain_gene 0.0006228 0.0060062 0.24 0.06
common_brain_gene_psy 0.0161286 0.3702811 6.28 1.61 
common_analysis_gene_psy_brain_all_site_both[[2]]%>% 
    kableExtra::kbl(caption = "R^2 metrics for all models in baseline") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
R^2 metrics for all models in baseline
model_names <- … marginal_rsq conditional_rsq
gene 0.0247904 0.3831416
brain 0.2079974 0.4226934
psy 0.1137145 0.4093088
brain_psy 0.2534047 0.4517052
gene_psy 0.1178976 0.4224429
brain_gene 0.2160364 0.4295476
brain_gene_psy 0.2569649 0.4588331 
followup_gene_psy_brain_table <- common_analysis_gene_psy_brain_all_site_both_followup[[1]]%>%
                                 mutate(corrected_percent = followup_plot_vec)%>%
                                 mutate(corrected_raw = followup_plot_vec_raw)


followup_gene_psy_brain_table%>% 
    kableExtra::kbl(caption = "Commonality analysis in followup") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
Commonality analysis in followup
variable_effects marginal_rsq conditional_rsq corrected_percent corrected_raw
unique_gene 0.0010708 0.0116287 0.0010708 0.11
unique_brain 0.1296642 0.0391959 0.1296642 12.97
unique_psy 0.0385946 0.0141564 0.0385946 3.86
common_brain_psy 0.0432285 -0.0007575 0.0432285 4.32
common_gene_psy 0.0005054 0.0010266 0.0005054 0.05
common_brain_gene 0.0018009 0.0056914 0.0018009 0.18
common_brain_gene_psy 0.0032148 0.4777824 0.0032148 0.32 
common_analysis_gene_psy_brain_all_site_both_followup[[2]]%>% 
    kableExtra::kbl(caption = "R^2 metrics for all models in followup") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
R^2 metrics for all models in followup
model_names <- … marginal_rsq conditional_rsq
gene 0.0065919 0.4961291
brain 0.1779084 0.5219121
psy 0.0855432 0.4922079
brain_psy 0.2170084 0.5370952
gene_psy 0.0884150 0.5095280
brain_gene 0.1794846 0.5345675
brain_gene_psy 0.2180792 0.5487239

3.3 Plot the venn diagram

baseline percentage of rsquare plot

overrideTriple=TRUE

venn_plot_baseline_percent <- draw.triple.venn(area1 = baseline_plot_vec_percent[2]+baseline_plot_vec_percent[6]+baseline_plot_vec_percent[4]+baseline_plot_vec_percent[7],
  area2 = baseline_plot_vec_percent[3]+baseline_plot_vec_percent[4]+baseline_plot_vec_percent[5]+baseline_plot_vec_percent[7],
  area3 = baseline_plot_vec_percent[1]+baseline_plot_vec_percent[5]+baseline_plot_vec_percent[6]+baseline_plot_vec_percent[7],
  n12 = baseline_plot_vec_percent[4]+baseline_plot_vec_percent[7],
  n13 = baseline_plot_vec_percent[6]+baseline_plot_vec_percent[7],
  n23 = baseline_plot_vec_percent[5]+baseline_plot_vec_percent[7],
  n123 = baseline_plot_vec_percent[7],
  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.1, 0.1,-0.2), # Modified
  cat.pos = c(5,10,10),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F)

grid.newpage()

#grid::grid.draw(venn_plot_baseline_percent)
#invisible(dev.off())

Without any labels

overrideTriple=TRUE

venn_plot_baseline_percent <- draw.triple.venn(area1 = baseline_plot_vec_percent[2]+baseline_plot_vec_percent[6]+baseline_plot_vec_percent[4]+baseline_plot_vec_percent[7],
  area2 = baseline_plot_vec_percent[3]+baseline_plot_vec_percent[4]+baseline_plot_vec_percent[5]+baseline_plot_vec_percent[7],
  area3 = baseline_plot_vec_percent[1]+baseline_plot_vec_percent[5]+baseline_plot_vec_percent[6]+baseline_plot_vec_percent[7],
  n12 = baseline_plot_vec_percent[4]+baseline_plot_vec_percent[7],
  n13 = baseline_plot_vec_percent[6]+baseline_plot_vec_percent[7],
  n23 = baseline_plot_vec_percent[5]+baseline_plot_vec_percent[7],
  n123 = baseline_plot_vec_percent[7],
#  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.1, 0.1,-0.1), # Modified
  cat.pos = c(5,10,-10),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F)

grid.newpage()

#grid::grid.draw(venn_plot_baseline_percent)
#invisible(dev.off())
#overrideTriple=TRUE

venn_plot_baseline_percent <- draw.triple.venn(area1 = baseline_plot_vec_percent[2]+baseline_plot_vec_percent[6]+baseline_plot_vec_percent[4]+baseline_plot_vec_percent[7],
  area2 = baseline_plot_vec_percent[3]+baseline_plot_vec_percent[4]+baseline_plot_vec_percent[5]+baseline_plot_vec_percent[7],
  area3 = baseline_plot_vec_percent[1]+baseline_plot_vec_percent[5]+baseline_plot_vec_percent[6]+baseline_plot_vec_percent[7],
  n12 = baseline_plot_vec_percent[4]+baseline_plot_vec_percent[7],
  n13 = baseline_plot_vec_percent[6]+baseline_plot_vec_percent[7],
  n23 = baseline_plot_vec_percent[5]+baseline_plot_vec_percent[7],
  n123 = baseline_plot_vec_percent[7],
  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("black", "black", "black"),
    filename = NULL,
  cex = 2, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(-0.1, -0.1,-0.1), # Modified
  #cat.pos = c(-30, 150,100),# Modified
  print.mode="percent"
  )

grid.newpage()

#grid::grid.draw(venn_plot_baseline_percent)
#invisible(dev.off())

3.3.1 baseline rsquare score plot

venn_plot_baseline_raw <- draw.triple.venn(area1 = baseline_plot_vec_raw[2]+baseline_plot_vec_raw[6]+baseline_plot_vec_raw[4]+baseline_plot_vec_raw[7],
  area2 = baseline_plot_vec_raw[3]+baseline_plot_vec_raw[4]+baseline_plot_vec_raw[5]+baseline_plot_vec_raw[7],
  area3 = baseline_plot_vec_raw[1]+baseline_plot_vec_raw[5]+baseline_plot_vec_raw[6]+baseline_plot_vec_raw[7],
  n12 = baseline_plot_vec_raw[4]+baseline_plot_vec_raw[7],
  n13 = baseline_plot_vec_raw[6]+baseline_plot_vec_raw[7],
  n23 = baseline_plot_vec_raw[5]+baseline_plot_vec_raw[7],
  n123 = baseline_plot_vec_raw[7],
  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("black", "black", "black"),
  filename = NULL,
  cex = 2, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(-0.1, -0.1,-0.1), # Modified
  #cat.pos = c(-30, 150,100),# Modified
  #print.mode="percent"
  )

grid.newpage()

#grid::grid.draw(venn_plot_baseline_raw)
#invisible(dev.off())

3.4 venn diagram for followup

venn_plot_followup_percent <- draw.triple.venn(area1 = followup_plot_vec_percentage[2]+followup_plot_vec_percentage[6]+followup_plot_vec_percentage[4]+followup_plot_vec_percentage[7],
  area2 = followup_plot_vec_percentage[3]+followup_plot_vec_percentage[4]+followup_plot_vec_percentage[5]+followup_plot_vec_percentage[7],
  area3 = followup_plot_vec_percentage[1]+followup_plot_vec_percentage[5]+followup_plot_vec_percentage[6]+followup_plot_vec_percentage[7],
  n12 = followup_plot_vec_percentage[4]+followup_plot_vec_percentage[7],
  n13 = followup_plot_vec_percentage[6]+followup_plot_vec_percentage[7],
  n23 = followup_plot_vec_percentage[5]+followup_plot_vec_percentage[7],
  n123 = followup_plot_vec_percentage[7],
  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("black", "black", "black"),##change the caption color
  filename = NULL,
  cex = 2, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(-0.1, -0.1,-0.1), # Modified
  #cat.pos = c(-30, 150,100),# Modified
  print.mode="percent")

grid.newpage()

#grid::grid.draw(venn_plot_followup_percent)
#invisible(dev.off())
overrideTriple=TRUE

venn_plot_baseline_percent <- draw.triple.venn(area1 = followup_plot_vec_percentage[2]+followup_plot_vec_percentage[6]+followup_plot_vec_percentage[4]+followup_plot_vec_percentage[7],
  area2 = followup_plot_vec_percentage[3]+followup_plot_vec_percentage[4]+followup_plot_vec_percentage[5]+followup_plot_vec_percentage[7],
  area3 = followup_plot_vec_percentage[1]+followup_plot_vec_percentage[5]+followup_plot_vec_percentage[6]+followup_plot_vec_percentage[7],
  n12 = followup_plot_vec_percentage[4]+followup_plot_vec_percentage[7],
  n13 = followup_plot_vec_percentage[6]+followup_plot_vec_percentage[7],
  n23 = followup_plot_vec_percentage[5]+followup_plot_vec_percentage[7],
  n123 = followup_plot_vec_percentage[7],
  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.1, 0.1,-0.15), # Modified
  cat.pos = c(5,10,-10),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F)

grid.newpage()

#grid::grid.draw(venn_plot_baseline_percent)
#invisible(dev.off())

Plot without label

overrideTriple=TRUE

venn_plot_baseline_percent <- draw.triple.venn(area1 = followup_plot_vec_percentage[2]+followup_plot_vec_percentage[6]+followup_plot_vec_percentage[4]+followup_plot_vec_percentage[7],
  area2 = followup_plot_vec_percentage[3]+followup_plot_vec_percentage[4]+followup_plot_vec_percentage[5]+followup_plot_vec_percentage[7],
  area3 = followup_plot_vec_percentage[1]+followup_plot_vec_percentage[5]+followup_plot_vec_percentage[6]+followup_plot_vec_percentage[7],
  n12 = followup_plot_vec_percentage[4]+followup_plot_vec_percentage[7],
  n13 = followup_plot_vec_percentage[6]+followup_plot_vec_percentage[7],
  n23 = followup_plot_vec_percentage[5]+followup_plot_vec_percentage[7],
  n123 = followup_plot_vec_percentage[7],
#  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.1, 0.1,-0.1), # Modified
  cat.pos = c(5,10,-10),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F)

grid.newpage()

#grid::grid.draw(venn_plot_baseline_percent)
#invisible(dev.off())
venn_plot_followup_raw <- draw.triple.venn(area1 = followup_plot_vec_raw[2]+followup_plot_vec_raw[6]+followup_plot_vec_raw[4]+followup_plot_vec_raw[7],
  area2 = followup_plot_vec_raw[3]+followup_plot_vec_raw[4]+followup_plot_vec_raw[5]+followup_plot_vec_raw[7],
  area3 = followup_plot_vec_raw[1]+followup_plot_vec_raw[5]+followup_plot_vec_raw[6]+followup_plot_vec_raw[7],
  n12 = followup_plot_vec_raw[4]+followup_plot_vec_raw[7],
  n13 = followup_plot_vec_raw[6]+followup_plot_vec_raw[7],
  n23 = followup_plot_vec_raw[5]+followup_plot_vec_raw[7],
  n123 = followup_plot_vec_raw[7],
  category = c("Brain", "Mental Health", "Genes"),
  fill = c("#009E73", "#D55E00", "#CC79A7"),
  lty = "dashed",
  cat.col = c("black", "black", "black"),##change the caption color
  filename = NULL,
  cex = 2, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(-0.1, -0.1,-0.1), # Modified
  #cat.pos = c(-30, 150,100),# Modified
  #print.mode="percent"
  )

grid.newpage()

#grid::grid.draw(venn_plot_followup_raw)
#invisible(dev.off())

4 commonality analysis between stacked brain scan, social demographic lifestyle, psychopathology and genetics that predicts cognition

4.1 Compute the commonality effects

common_analysis_gene_psy_brain_ses <- function(data_input=data_all_baseline){

  print("gene_psy_brain_ses_model")
  
  gene_psy_brain_ses_model <- lmer(gfactor ~ mental_savg+mental_cws+brain_savg+ brain_cws+ gene_savg_favg+gene_cws_cwf+sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_gene_psy_brain_ses_model <- performance::r2(gene_psy_brain_ses_model)




  print("gene_psy_brain_model")
  
  gene_psy_brain_model <- lmer(gfactor ~ mental_savg+mental_cws+brain_savg+ brain_cws+ gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_gene_psy_brain_model <- performance::r2(gene_psy_brain_model)
  

 print("gene_brain_ses_model")
  
  gene_brain_ses_model <- lmer(gfactor ~ brain_savg+ brain_cws+gene_savg_favg+gene_cws_cwf+sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_gene_brain_ses_model <- performance::r2(gene_brain_ses_model)


print("gene_psy_ses_model")
  
  gene_psy_ses_model <- lmer(gfactor ~  mental_savg+mental_cws+gene_savg_favg+gene_cws_cwf+sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_gene_psy_ses_model <- performance::r2(gene_psy_ses_model)


print("psy_brain_ses_model")
  
psy_brain_ses_model <- lmer(gfactor ~ mental_savg+mental_cws+brain_savg+ brain_cws+ sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_psy_brain_ses_model <- performance::r2(psy_brain_ses_model)






print("gene_ses_model")
  
  gene_ses_model <- lmer(gfactor ~ gene_savg_favg+gene_cws_cwf+sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_gene_ses_model <- performance::r2(gene_ses_model)


print("psy_ses_model")
  
  psy_ses_model <- lmer(gfactor ~ mental_savg+mental_cws+sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_psy_ses_model <- performance::r2(psy_ses_model)



print("brain_ses_model")
  
  brain_ses_model <- lmer(gfactor ~ brain_savg+ brain_cws+sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_brain_ses_model <- performance::r2(brain_ses_model)

    print("brain_psy_model") 
  brain_psy_model <- lmer(gfactor ~ mental_savg+mental_cws+brain_savg+ brain_cws+(1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_brain_psy <- performance::r2(brain_psy_model)
 

  print("gene_psy_model")
  ## this model has singularity problem
  gene_psy_model <- lmer(gfactor ~mental_savg+mental_cws+gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_gene_psy <- performance::r2(gene_psy_model)


   print("brain_gene_model") 
  brain_gene_model <- lmer(gfactor ~ brain_savg+ brain_cws+gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_brain_gene <- performance::r2(brain_gene_model)

  
  
  
  print("ses_model")
  
  ses_model <- lmer(gfactor ~ sdl_savg+sdl_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
# runs very slow with this multilevel model
 # rsq_full_model <- MuMIn::r.squaredGLMM(full_model)
rsq_ses_model <- performance::r2(ses_model)

  
  
  
  print("brain_model")
  brain_model <- lmer(gfactor ~ brain_savg+ brain_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_brain <- performance::r2(brain_model)


  print("psy_model")
  psy_model <- lmer(gfactor ~  mental_savg+mental_cws+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsq_psy <- performance::r2(psy_model)

    print("gene_model")
  gene_model <- lmer(gfactor ~ gene_savg_favg+gene_cws_cwf+ (1|SITE_ID_L:REL_FAMILY_ID),data = data_input)
  rsp_gene <- performance::r2(gene_model)

### marginal    
    
  ### unique effects
  unique_gene_margin <- rsq_gene_psy_brain_ses_model$R2_marginal-rsq_psy_brain_ses_model$R2_marginal
 
   unique_brain_margin <- rsq_gene_psy_brain_ses_model$R2_marginal-rsq_gene_psy_ses_model$R2_marginal
  
   unique_psy_margin <- rsq_gene_psy_brain_ses_model$R2_marginal-rsq_gene_brain_ses_model$R2_marginal
  
   unique_ses_margin <- rsq_gene_psy_brain_ses_model$R2_marginal-rsq_gene_psy_brain_model$R2_marginal
### common effects between two variables
common_brain_psy_margin <- rsq_gene_brain_ses_model$R2_marginal+rsq_gene_psy_ses_model$R2_marginal-rsq_gene_ses_model$R2_marginal-rsq_gene_psy_brain_ses_model$R2_marginal
  
common_gene_psy_margin <- rsq_gene_brain_ses_model$R2_marginal+rsq_psy_brain_ses_model$R2_marginal-rsq_brain_ses_model$R2_marginal-rsq_gene_psy_brain_ses_model$R2_marginal

common_brain_gene_margin <- rsq_psy_brain_ses_model$R2_marginal+rsq_gene_psy_ses_model$R2_marginal-rsq_psy_ses_model$R2_marginal-rsq_gene_psy_brain_ses_model$R2_marginal

common_brain_ses_margin <- rsq_gene_psy_brain_model$R2_marginal+rsq_gene_psy_ses_model$R2_marginal-rsq_gene_psy$R2_marginal-rsq_gene_psy_brain_ses_model$R2_marginal

common_psy_ses_margin <- rsq_gene_psy_brain_model$R2_marginal+rsq_gene_brain_ses_model$R2_marginal-rsq_brain_gene$R2_marginal-rsq_gene_psy_brain_ses_model$R2_marginal

common_gene_ses_margin <- rsq_gene_psy_brain_model$R2_marginal+rsq_psy_brain_ses_model$R2_marginal-rsq_brain_psy$R2_marginal-rsq_gene_psy_brain_ses_model$R2_marginal
  # common effects between three variables
common_brain_gene_psy_margin <-rsq_brain_ses_model$R2_marginal+rsq_gene_ses_model$R2_marginal+rsq_psy_ses_model$R2_marginal-rsq_gene_brain_ses_model$R2_marginal-rsq_gene_psy_ses_model$R2_marginal-rsq_psy_brain_ses_model$R2_marginal-rsq_ses_model$R2_marginal+rsq_gene_psy_brain_ses_model$R2_marginal

common_ses_gene_psy_margin <-rsq_brain_ses_model$R2_marginal+rsq_brain_gene$R2_marginal+rsq_brain_psy$R2_marginal-rsq_gene_psy_brain_model$R2_marginal-rsq_gene_brain_ses_model$R2_marginal-rsq_psy_brain_ses_model$R2_marginal-rsq_brain$R2_marginal+rsq_gene_psy_brain_ses_model$R2_marginal

common_brain_ses_psy_margin <-rsq_brain_gene$R2_marginal+rsq_gene_ses_model$R2_marginal+rsq_gene_psy$R2_marginal-rsq_gene_psy_brain_model$R2_marginal-rsq_gene_brain_ses_model$R2_marginal-rsq_gene_psy_ses_model$R2_marginal-rsp_gene$R2_marginal+rsq_gene_psy_brain_ses_model$R2_marginal

common_brain_gene_ses_margin <-rsq_brain_psy$R2_marginal+rsq_gene_psy$R2_marginal+rsq_psy_ses_model$R2_marginal-rsq_gene_psy_brain_model$R2_marginal-rsq_gene_psy_ses_model$R2_marginal-rsq_psy_brain_ses_model$R2_marginal-rsq_psy$R2_marginal+rsq_gene_psy_brain_ses_model$R2_marginal

# common effects between four variables

common_brain_gene_psy_ses_margin <-rsq_ses_model$R2_marginal+rsq_brain$R2_marginal+rsq_psy$R2_marginal+rsp_gene$R2_marginal-rsq_gene_ses_model$R2_marginal-rsq_psy_ses_model$R2_marginal-rsq_brain_ses_model$R2_marginal-rsq_brain_psy$R2_marginal-rsq_gene_psy$R2_marginal-rsq_brain_gene$R2_marginal+rsq_gene_psy_brain_model$R2_marginal+rsq_gene_brain_ses_model$R2_marginal+rsq_gene_psy_ses_model$R2_marginal+rsq_psy_brain_ses_model$R2_marginal - rsq_gene_psy_brain_ses_model$R2_marginal 
  
  ### conditional
  
     
       ### unique effects
unique_gene_conditional <- rsq_gene_psy_brain_ses_model$R2_conditional-rsq_psy_brain_ses_model$R2_conditional

unique_brain_conditional <- rsq_gene_psy_brain_ses_model$R2_conditional-rsq_gene_psy_ses_model$R2_conditional

unique_psy_conditional <- rsq_gene_psy_brain_ses_model$R2_conditional-rsq_gene_brain_ses_model$R2_conditional

unique_ses_conditional <- rsq_gene_psy_brain_ses_model$R2_conditional-rsq_gene_psy_brain_model$R2_conditional
### common effects between two variables
common_brain_psy_conditional <- rsq_gene_brain_ses_model$R2_conditional+rsq_gene_psy_ses_model$R2_conditional-rsq_gene_ses_model$R2_conditional-rsq_gene_psy_brain_ses_model$R2_conditional

common_gene_psy_conditional <- rsq_gene_brain_ses_model$R2_conditional+rsq_psy_brain_ses_model$R2_conditional-rsq_brain_ses_model$R2_conditional-rsq_gene_psy_brain_ses_model$R2_conditional

common_brain_gene_conditional <- 
rsq_psy_brain_ses_model$R2_conditional+rsq_gene_psy_ses_model$R2_conditional-rsq_psy_ses_model$R2_conditional-rsq_gene_psy_brain_ses_model$R2_conditional

common_brain_ses_conditional <- rsq_gene_psy_brain_model$R2_conditional+rsq_gene_psy_ses_model$R2_conditional-rsq_gene_psy$R2_conditional-rsq_gene_psy_brain_ses_model$R2_conditional

common_psy_ses_conditional <- rsq_gene_psy_brain_model$R2_conditional+rsq_gene_brain_ses_model$R2_conditional-rsq_brain_gene$R2_conditional-rsq_gene_psy_brain_ses_model$R2_conditional

common_gene_ses_conditional <- rsq_gene_psy_brain_model$R2_conditional+rsq_psy_brain_ses_model$R2_conditional-rsq_brain_psy$R2_conditional-rsq_gene_psy_brain_ses_model$R2_conditional
  # common effects between three variables

common_brain_gene_psy_conditional <-rsq_brain_ses_model$R2_conditional+rsq_gene_ses_model$R2_conditional+rsq_psy_ses_model$R2_conditional-rsq_gene_brain_ses_model$R2_conditional-rsq_gene_psy_ses_model$R2_conditional-rsq_psy_brain_ses_model$R2_conditional-rsq_ses_model$R2_conditional+rsq_gene_psy_brain_ses_model$R2_conditional

common_ses_gene_psy_conditional <-rsq_brain_ses_model$R2_conditional+rsq_brain_gene$R2_conditional+rsq_brain_psy$R2_conditional-rsq_gene_psy_brain_model$R2_conditional-rsq_gene_brain_ses_model$R2_conditional-rsq_psy_brain_ses_model$R2_conditional-rsq_brain$R2_conditional+rsq_gene_psy_brain_ses_model$R2_conditional

common_brain_ses_psy_conditional <-rsq_brain_gene$R2_conditional+rsq_gene_ses_model$R2_conditional+rsq_gene_psy$R2_conditional-rsq_gene_psy_brain_model$R2_conditional-rsq_gene_brain_ses_model$R2_conditional-rsq_gene_psy_ses_model$R2_conditional-rsp_gene$R2_conditional+rsq_gene_psy_brain_ses_model$R2_conditional

common_brain_gene_ses_conditional <-rsq_brain_psy$R2_conditional+rsq_gene_psy$R2_conditional+rsq_psy_ses_model$R2_conditional-rsq_gene_psy_brain_model$R2_conditional-rsq_gene_psy_ses_model$R2_conditional-rsq_psy_brain_ses_model$R2_conditional-rsq_psy$R2_conditional+rsq_gene_psy_brain_ses_model$R2_conditional

# common effects between four variables
common_brain_gene_psy_ses_conditional <-rsq_ses_model$R2_conditional+rsq_brain$R2_conditional+rsq_psy$R2_conditional+rsp_gene$R2_conditional-rsq_gene_ses_model$R2_conditional-rsq_psy_ses_model$R2_conditional-rsq_brain_ses_model$R2_conditional-rsq_brain_psy$R2_conditional-rsq_gene_psy$R2_conditional-rsq_brain_gene$R2_conditional+rsq_gene_psy_brain_model$R2_conditional+rsq_gene_brain_ses_model$R2_conditional+rsq_gene_psy_ses_model$R2_conditional+rsq_psy_brain_ses_model$R2_conditional - rsq_gene_psy_brain_ses_model$R2_conditional 

## anova analysis

ses_significance <- anova(gene_psy_brain_ses_model,gene_psy_brain_model)

psy_significance <- anova(gene_psy_brain_ses_model,gene_brain_ses_model)

brain_significance <- anova(gene_psy_brain_ses_model,gene_psy_ses_model)

gene_significance <- anova(gene_psy_brain_ses_model,psy_brain_ses_model)

psy_ses_ses <- anova(psy_ses_model,psy_model)
psy_ses_psy <- anova(psy_ses_model,ses_model)

brain_psy_brain <- anova(brain_psy_model,psy_model)
brain_psy_psy <- anova(brain_psy_model,brain_model)

gene_psy_psy <- anova(gene_psy_model, gene_model)
gene_psy_gene <- anova(gene_psy_model, psy_model)

  output_common_tibble <- tibble(variable_effects = c("unique_gene",
                                               "unique_brain",
                                              "unique_psy",
                                              "unique_ses",
                                              "common_brain_psy",
                                              "common_gene_psy",
                                              "common_brain_gene",
                                              "common_brain_ses",
                                              "common_psy_ses",
                                              "common_gene_ses",
                                              "common_brain_gene_psy",
                                              "common_ses_gene_psy",
                                              "common_brain_ses_psy",
                                              "common_brain_gene_ses",
                                              "common_brain_gene_psy_ses"),
                          marginal_rsq = c(unique_gene_margin,
                                              unique_brain_margin,
                                              unique_psy_margin,
                                           unique_ses_margin,
                                              common_brain_psy_margin,
                                              common_gene_psy_margin,
                                              common_brain_gene_margin,
                                           common_brain_ses_margin,
                                           common_psy_ses_margin,
                                           common_gene_ses_margin,
                                              common_brain_gene_psy_margin,
                                          common_ses_gene_psy_margin,
                                          common_brain_ses_psy_margin,
                                          common_brain_gene_ses_margin,
                                          common_brain_gene_psy_ses_margin),
                          conditional_rsq = c(unique_gene_conditional,
                                              unique_brain_conditional,
                                              unique_psy_conditional,
                                              unique_ses_conditional,
                                              common_brain_psy_conditional,
                                              common_gene_psy_conditional,
                                              common_brain_gene_conditional,
                                              common_brain_ses_conditional,
                                              common_psy_ses_conditional,
                                              common_gene_ses_conditional,
                                              common_brain_gene_psy_conditional,
                                              common_ses_gene_psy_conditional,
                                              common_brain_ses_psy_conditional,
                                              common_brain_gene_ses_conditional,
                                              common_brain_gene_psy_ses_conditional))
  output_rsq_tibble <- tibble(model_names<- c("gene",
                                               "brain",
                                              "psy",
                                              "ses",
                                              "brain_psy",
                                              "gene_psy",
                                              "brain_gene",
                                              "brain_ses",
                                              "psy_ses",
                                              "gene_ses",
                                              "brain_gene_psy",
                                              "ses_gene_psy",
                                              "brain_ses_psy",
                                              "brain_gene_ses",
                                              "brain_gene_psy_ses"),
                          marginal_rsq = c(rsp_gene$R2_marginal,
                                              rsq_brain$R2_marginal,
                                              rsq_psy$R2_marginal,
                                           rsq_ses_model$R2_marginal,
                                              rsq_brain_psy$R2_marginal,
                                              rsq_gene_psy$R2_marginal,
                                              rsq_brain_gene$R2_marginal,
                                           rsq_brain_ses_model$R2_marginal,
                                           rsq_psy_ses_model$R2_marginal,
                                           rsq_gene_ses_model$R2_marginal,
                                              rsq_gene_psy_brain_model$R2_marginal,
                                           rsq_gene_psy_ses_model$R2_marginal,
                                           rsq_psy_brain_ses_model$R2_marginal,
                                          rsq_gene_brain_ses_model$R2_marginal,
                                          rsq_gene_psy_brain_ses_model$R2_marginal),
                          conditional_rsq = c(rsp_gene$R2_conditional,
                                              rsq_brain$R2_conditional,
                                              rsq_psy$R2_conditional,
                                           rsq_ses_model$R2_conditional,
                                              rsq_brain_psy$R2_conditional,
                                              rsq_gene_psy$R2_conditional,
                                              rsq_brain_gene$R2_conditional,
                                           rsq_brain_ses_model$R2_conditional,
                                           rsq_psy_ses_model$R2_conditional,
                                           rsq_gene_ses_model$R2_conditional,
                                              rsq_gene_psy_brain_model$R2_conditional,
                                           rsq_gene_psy_ses_model$R2_conditional,
                                           rsq_psy_brain_ses_model$R2_conditional,
                                          rsq_gene_brain_ses_model$R2_conditional,
                                          rsq_gene_psy_brain_ses_model$R2_conditional))
  
  return(list(output_common_tibble=output_common_tibble,
              output_rsq_tibble=output_rsq_tibble,
              gene_psy_brain_ses_model=gene_psy_brain_ses_model,
              gene_psy_brain_model=gene_psy_brain_model,
              gene_brain_ses_model=gene_brain_ses_model,
              gene_psy_ses_model=gene_psy_ses_model,
              psy_brain_ses_model=psy_brain_ses_model,
              gene_ses_model=gene_ses_model,
              psy_ses_model=psy_ses_model,
              brain_ses_model=brain_ses_model,
              brain_psy_model=brain_psy_model,
              gene_psy_model=gene_psy_model,
              brain_gene_model=brain_gene_model,
              ses_model=ses_model,
              brain_model=brain_model,
              psy_model=psy_model,
              gene_model=gene_model,
              ses_significance=ses_significance,
              psy_significance=psy_significance,
              brain_significance=brain_significance,
              gene_significance=gene_significance,
              psy_ses_ses = psy_ses_ses,
              psy_ses_psy=psy_ses_psy,
              brain_psy_brain = brain_psy_brain,
              brain_psy_psy = brain_psy_psy,
              gene_psy_psy = gene_psy_psy,
              gene_psy_gene = gene_psy_gene
             ))
  }
common_analysis_gene_psy_brain_ses_baseline <-common_analysis_gene_psy_brain_ses(data_input=data_all_baseline)
## [1] "gene_psy_brain_ses_model"
## [1] "gene_psy_brain_model"
## [1] "gene_brain_ses_model"
## [1] "gene_psy_ses_model"
## [1] "psy_brain_ses_model"
## [1] "gene_ses_model"
## [1] "psy_ses_model"
## [1] "brain_ses_model"
## [1] "brain_psy_model"
## [1] "gene_psy_model"
## [1] "brain_gene_model"
## [1] "ses_model"
## [1] "brain_model"
## [1] "psy_model"
## [1] "gene_model"
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
common_analysis_gene_psy_brain_ses_followup <-common_analysis_gene_psy_brain_ses(data_input=data_all_followup)
## [1] "gene_psy_brain_ses_model"
## [1] "gene_psy_brain_model"
## [1] "gene_brain_ses_model"
## [1] "gene_psy_ses_model"
## [1] "psy_brain_ses_model"
## [1] "gene_ses_model"
## [1] "psy_ses_model"
## [1] "brain_ses_model"
## [1] "brain_psy_model"
## [1] "gene_psy_model"
## [1] "brain_gene_model"
## [1] "ses_model"
## [1] "brain_model"
## [1] "psy_model"
## [1] "gene_model"
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)
## refitting model(s) with ML (instead of REML)

checking the significant level of the unique effects of the four feature model

## baseline

common_analysis_gene_psy_brain_ses_baseline[["ses_significance"]]
## Data: data_input
## Models:
## gene_psy_brain_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar   AIC   BIC  logLik deviance  Chisq Df
## gene_psy_brain_model        9 13501 13561 -6741.6    13483          
## gene_psy_brain_ses_model   11 13287 13360 -6632.7    13265 217.79  2
##                                     Pr(>Chisq)    
## gene_psy_brain_model                              
## gene_psy_brain_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_baseline[["psy_significance"]]
## Data: data_input
## Models:
## gene_brain_ses_model: gfactor ~ brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar   AIC   BIC  logLik deviance  Chisq Df
## gene_brain_ses_model        9 13452 13511 -6716.7    13434          
## gene_psy_brain_ses_model   11 13287 13360 -6632.7    13265 168.15  2
##                                     Pr(>Chisq)    
## gene_brain_ses_model                              
## gene_psy_brain_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_baseline[["brain_significance"]]
## Data: data_input
## Models:
## gene_psy_ses_model: gfactor ~ mental_savg + mental_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar   AIC   BIC  logLik deviance  Chisq Df
## gene_psy_ses_model          9 14061 14121 -7021.6    14043          
## gene_psy_brain_ses_model   11 13287 13360 -6632.7    13265 777.85  2
##                                     Pr(>Chisq)    
## gene_psy_ses_model                                
## gene_psy_brain_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_baseline[["gene_significance"]]
## Data: data_input
## Models:
## psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar   AIC   BIC  logLik deviance Chisq Df
## psy_brain_ses_model         9 13312 13371 -6646.7    13294         
## gene_psy_brain_ses_model   11 13287 13360 -6632.7    13265  28.1  2
##                            Pr(>Chisq)    
## psy_brain_ses_model                      
## gene_psy_brain_ses_model 0.0000007909 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["ses_significance"]]
## Data: data_input
## Models:
## gene_psy_brain_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar    AIC    BIC  logLik deviance  Chisq Df
## gene_psy_brain_model        9 7788.1 7843.1 -3885.1   7770.1          
## gene_psy_brain_ses_model   11 7647.7 7714.9 -3812.8   7625.7 144.42  2
##                                     Pr(>Chisq)    
## gene_psy_brain_model                              
## gene_psy_brain_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["psy_significance"]]
## Data: data_input
## Models:
## gene_brain_ses_model: gfactor ~ brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar    AIC    BIC  logLik deviance  Chisq Df
## gene_brain_ses_model        9 7729.3 7784.3 -3855.7   7711.3          
## gene_psy_brain_ses_model   11 7647.7 7714.9 -3812.8   7625.7 85.655  2
##                                     Pr(>Chisq)    
## gene_brain_ses_model                              
## gene_psy_brain_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["brain_significance"]]
## Data: data_input
## Models:
## gene_psy_ses_model: gfactor ~ mental_savg + mental_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar    AIC    BIC  logLik deviance Chisq Df
## gene_psy_ses_model          9 8084.3 8139.3 -4033.1   8066.3         
## gene_psy_brain_ses_model   11 7647.7 7714.9 -3812.8   7625.7 440.6  2
##                                     Pr(>Chisq)    
## gene_psy_ses_model                                
## gene_psy_brain_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["gene_significance"]]
## Data: data_input
## Models:
## psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_brain_ses_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + gene_savg_favg + gene_cws_cwf + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                          npar    AIC    BIC  logLik deviance Chisq Df
## psy_brain_ses_model         9 7660.4 7715.3 -3821.2   7642.4         
## gene_psy_brain_ses_model   11 7647.7 7714.9 -3812.8   7625.7 16.68  2
##                          Pr(>Chisq)    
## psy_brain_ses_model                    
## gene_psy_brain_ses_model  0.0002388 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

checking the significance level of the two features models

common_analysis_gene_psy_brain_ses_baseline[["psy_ses_ses"]] 
## Data: data_input
## Models:
## psy_model: gfactor ~ mental_savg + mental_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## psy_ses_model: gfactor ~ mental_savg + mental_cws + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##               npar   AIC   BIC  logLik deviance  Chisq Df            Pr(>Chisq)
## psy_model        5 14490 14523 -7240.1    14480                                
## psy_ses_model    7 14090 14136 -7038.0    14076 404.19  2 < 0.00000000000000022
##                  
## psy_model        
## psy_ses_model ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 common_analysis_gene_psy_brain_ses_baseline[["psy_ses_psy"]]       
## Data: data_input
## Models:
## ses_model: gfactor ~ sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## psy_ses_model: gfactor ~ mental_savg + mental_cws + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##               npar   AIC   BIC logLik deviance  Chisq Df            Pr(>Chisq)
## ses_model        5 14376 14409  -7183    14366                                
## psy_ses_model    7 14090 14136  -7038    14076 290.05  2 < 0.00000000000000022
##                  
## ses_model        
## psy_ses_model ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["psy_ses_ses"]] 
## Data: data_input
## Models:
## psy_model: gfactor ~ mental_savg + mental_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## psy_ses_model: gfactor ~ mental_savg + mental_cws + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##               npar    AIC    BIC  logLik deviance  Chisq Df
## psy_model        5 8314.5 8345.1 -4152.3   8304.5          
## psy_ses_model    7 8102.0 8144.7 -4044.0   8088.0 216.57  2
##                          Pr(>Chisq)    
## psy_model                              
## psy_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 common_analysis_gene_psy_brain_ses_followup[["psy_ses_psy"]]       
## Data: data_input
## Models:
## ses_model: gfactor ~ sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## psy_ses_model: gfactor ~ mental_savg + mental_cws + sdl_savg + sdl_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##               npar    AIC    BIC  logLik deviance  Chisq Df
## ses_model        5 8257.5 8288.0 -4123.7   8247.5          
## psy_ses_model    7 8102.0 8144.7 -4044.0   8088.0 159.54  2
##                          Pr(>Chisq)    
## ses_model                              
## psy_ses_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_baseline[["brain_psy_brain"]]               
## Data: data_input
## Models:
## psy_model: gfactor ~ mental_savg + mental_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## brain_psy_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                 npar   AIC   BIC  logLik deviance  Chisq Df
## psy_model          5 14490 14523 -7240.1    14480          
## brain_psy_model    7 13532 13579 -6759.3    13518 961.59  2
##                            Pr(>Chisq)    
## psy_model                                
## brain_psy_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_baseline[["brain_psy_psy"]]          
## Data: data_input
## Models:
## brain_model: gfactor ~ brain_savg + brain_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## brain_psy_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                 npar   AIC   BIC  logLik deviance  Chisq Df
## brain_model        5 13855 13888 -6922.5    13845          
## brain_psy_model    7 13532 13579 -6759.3    13518 326.42  2
##                            Pr(>Chisq)    
## brain_model                              
## brain_psy_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 common_analysis_gene_psy_brain_ses_followup[["brain_psy_brain"]]               
## Data: data_input
## Models:
## psy_model: gfactor ~ mental_savg + mental_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## brain_psy_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                 npar    AIC    BIC  logLik deviance  Chisq Df
## psy_model          5 8314.5 8345.1 -4152.3   8304.5          
## brain_psy_model    7 7800.9 7843.7 -3893.5   7786.9 517.58  2
##                            Pr(>Chisq)    
## psy_model                                
## brain_psy_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["brain_psy_psy"]]  
## Data: data_input
## Models:
## brain_model: gfactor ~ brain_savg + brain_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## brain_psy_model: gfactor ~ mental_savg + mental_cws + brain_savg + brain_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
##                 npar    AIC    BIC  logLik deviance  Chisq Df
## brain_model        5 7951.4 7981.9 -3970.7   7941.4          
## brain_psy_model    7 7800.9 7843.7 -3893.5   7786.9 154.45  2
##                            Pr(>Chisq)    
## brain_model                              
## brain_psy_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_baseline[["gene_psy_psy"]]               
## Data: data_input
## Models:
## gene_model: gfactor ~ gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_model: gfactor ~ mental_savg + mental_cws + gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
##                npar   AIC   BIC  logLik deviance  Chisq Df
## gene_model        5 15020 15053 -7504.9    15010          
## gene_psy_model    7 14456 14502 -7221.0    14442 567.78  2
##                           Pr(>Chisq)    
## gene_model                              
## gene_psy_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_baseline[["gene_psy_gene"]]      
## Data: data_input
## Models:
## psy_model: gfactor ~ mental_savg + mental_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_model: gfactor ~ mental_savg + mental_cws + gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
##                npar   AIC   BIC  logLik deviance  Chisq Df     Pr(>Chisq)    
## psy_model         5 14490 14523 -7240.1    14480                             
## gene_psy_model    7 14456 14502 -7221.0    14442 38.114  2 0.000000005291 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["gene_psy_psy"]]               
## Data: data_input
## Models:
## gene_model: gfactor ~ gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_model: gfactor ~ mental_savg + mental_cws + gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
##                npar    AIC    BIC  logLik deviance  Chisq Df
## gene_model        5 8577.8 8608.3 -4283.9   8567.8          
## gene_psy_model    7 8295.4 8338.2 -4140.7   8281.4 286.33  2
##                           Pr(>Chisq)    
## gene_model                              
## gene_psy_model < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
common_analysis_gene_psy_brain_ses_followup[["gene_psy_gene"]]     
## Data: data_input
## Models:
## psy_model: gfactor ~ mental_savg + mental_cws + (1 | SITE_ID_L:REL_FAMILY_ID)
## gene_psy_model: gfactor ~ mental_savg + mental_cws + gene_savg_favg + gene_cws_cwf + (1 | SITE_ID_L:REL_FAMILY_ID)
##                npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)    
## psy_model         5 8314.5 8345.1 -4152.3   8304.5                         
## gene_psy_model    7 8295.4 8338.2 -4140.7   8281.4 23.085  2 0.00000971 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

4.2 processing the results and plotting

This chunk needs to be checked manually for every analysis.

baseline_gene_psy_brain_ses_vec <- common_analysis_gene_psy_brain_ses_baseline[[1]]$marginal_rsq

### get rid of the negative values
baseline_gene_psy_brain_ses_vec_corrected <- baseline_gene_psy_brain_ses_vec 
baseline_gene_psy_brain_ses_vec_corrected[14] <- 0


baseline_gene_psy_brain_ses_vec_corrected_percent <- baseline_gene_psy_brain_ses_vec_corrected/sum(baseline_gene_psy_brain_ses_vec_corrected)*100 
baseline_gene_psy_brain_ses_vec_corrected_percent <- round(baseline_gene_psy_brain_ses_vec_corrected_percent,2)
print(baseline_gene_psy_brain_ses_vec_corrected_percent)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        0.86       36.34        7.31       10.97        7.33        0.08 
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        0.27       11.81        6.86        0.37        0.14        1.47 
## Marginal R2 Marginal R2 Marginal R2 
##       10.74        0.00        5.45
baseline_gene_psy_brain_ses_vec_corrected_raw <- baseline_gene_psy_brain_ses_vec_corrected*100 
#baseline_gene_psy_brain_ses_vec_corrected_raw <- round(baseline_gene_psy_brain_ses_vec_corrected_raw,2)
print(baseline_gene_psy_brain_ses_vec_corrected_raw)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##  0.24965574 10.49551818  2.11136381  3.16824582  2.11574846  0.02357059 
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##  0.07822787  3.41121689  1.98148702  0.10637145  0.03948742  0.42430725 
## Marginal R2 Marginal R2 Marginal R2 
##  3.10211387  0.00000000  1.57337195
### manually assign values because there is some weird thing with label, to get the rounding at the plots right
#baseline_gene_psy_brain_ses_vec_corrected_raw[1]<- 0.02496
#baseline_gene_psy_brain_ses_vec_corrected_raw[6]<- 0.022
#baseline_gene_psy_brain_ses_vec_corrected_raw[7]<- 0.081


followup_gene_psy_brain_ses_vec <- common_analysis_gene_psy_brain_ses_followup[[1]]$marginal_rsq



### get rid of the negative values
followup_gene_psy_brain_ses_vec_corrected <- followup_gene_psy_brain_ses_vec 
followup_gene_psy_brain_ses_vec_corrected[10] <- 0


followup_gene_psy_brain_ses_vec_corrected_percentage <- followup_gene_psy_brain_ses_vec_corrected/sum(followup_gene_psy_brain_ses_vec_corrected)*100
followup_gene_psy_brain_ses_vec_corrected_percentage <- round(followup_gene_psy_brain_ses_vec_corrected_percentage,2)
print(followup_gene_psy_brain_ses_vec_corrected_percentage)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        0.48       40.74        7.67       15.07        8.52        0.11 
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##        0.53        9.71        7.35        0.00        0.13        0.09 
## Marginal R2 Marginal R2 Marginal R2 
##        8.30        0.17        1.12
followup_gene_psy_brain_ses_vec_corrected_raw <- followup_gene_psy_brain_ses_vec_corrected*100 
#followup_gene_psy_brain_ses_vec_corrected_raw <- round(followup_gene_psy_brain_ses_vec_corrected_raw,2)
print(followup_gene_psy_brain_ses_vec_corrected_raw)
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##  0.12406442 10.47043256  1.97104483  3.87354361  2.19029844  0.02860481 
## Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 Marginal R2 
##  0.13564405  2.49598615  1.88841052  0.00000000  0.03319219  0.02193180 
## Marginal R2 Marginal R2 Marginal R2 
##  2.13255475  0.04445074  0.28828519

4.3 table results

baseline_table <- common_analysis_gene_psy_brain_ses_baseline[[1]]%>%
                  mutate(corrected_percent = baseline_gene_psy_brain_ses_vec_corrected_percent)%>% 
                  mutate(corrected_raw = baseline_gene_psy_brain_ses_vec_corrected_raw)




baseline_table%>% 
    kableExtra::kbl(caption = "Commonality analysis in baseline") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
Commonality analysis in baseline
variable_effects marginal_rsq conditional_rsq corrected_percent corrected_raw
unique_gene 0.0024966 0.0080207 0.86 0.2496557
unique_brain 0.1049552 0.0385829 36.34 10.4955182
unique_psy 0.0211136 0.0241596 7.31 2.1113638
unique_ses 0.0316825 0.0074155 10.97 3.1682458
common_brain_psy 0.0211575 0.0155249 7.33 2.1157485
common_gene_psy 0.0002357 0.0008660 0.08 0.0235706
common_brain_gene 0.0007823 0.0053520 0.27 0.0782279
common_brain_ses 0.0341122 -0.0021927 11.81 3.4112169
common_psy_ses 0.0198149 0.0051258 6.86 1.9814870
common_gene_ses 0.0010637 -0.0008929 0.37 0.1063715
common_brain_gene_psy 0.0003949 0.0015975 0.14 0.0394874
common_ses_gene_psy 0.0042431 -0.0011396 1.47 0.4243072
common_brain_ses_psy 0.0310211 -0.0055090 10.74 3.1021139
common_brain_gene_ses -0.0001595 0.0006542 0.00 0.0000000
common_brain_gene_psy_ses 0.0157337 0.3686836 5.45 1.5733719 
common_analysis_gene_psy_brain_ses_baseline[[2]]%>% 
    kableExtra::kbl(caption = "R^2 metrics for all models in baseline") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
R^2 metrics for all models in baseline
model_names <- … marginal_rsq conditional_rsq
gene 0.0247904 0.3831416
brain 0.2079974 0.4226934
psy 0.1137145 0.4093088
ses 0.1375117 0.3721449
brain_psy 0.2534047 0.4517052
gene_psy 0.1178976 0.4224429
brain_gene 0.2160364 0.4295476
brain_ses 0.2648015 0.4332022
psy_ses 0.1804134 0.4142929
gene_ses 0.1414211 0.3879812
brain_gene_psy 0.2569649 0.4588331
ses_gene_psy 0.1836922 0.4276656
brain_ses_psy 0.2861508 0.4582278
brain_gene_ses 0.2675338 0.4420889
brain_gene_psy_ses 0.2886474 0.4662486 
followup_table <- common_analysis_gene_psy_brain_ses_followup[[1]]%>% 
                 mutate(correted_percent = followup_gene_psy_brain_ses_vec_corrected_percentage)%>%
                 mutate(corrected_raw = followup_gene_psy_brain_ses_vec_corrected_raw)




followup_table%>% 
    kableExtra::kbl(caption = "Commonality analysis in followup") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
Commonality analysis in followup
variable_effects marginal_rsq conditional_rsq correted_percent corrected_raw
unique_gene 0.0012406 0.0110840 0.48 0.1240644
unique_brain 0.1047043 0.0416755 40.74 10.4704326
unique_psy 0.0197104 0.0151567 7.67 1.9710448
unique_ses 0.0387354 0.0017974 15.07 3.8735436
common_brain_psy 0.0219030 0.0083483 8.52 2.1902984
common_gene_psy 0.0002860 0.0004074 0.11 0.0286048
common_brain_gene 0.0013564 0.0045319 0.53 0.1356440
common_brain_ses 0.0249599 -0.0024797 9.71 2.4959861
common_psy_ses 0.0188841 -0.0010003 7.35 1.8884105
common_gene_ses -0.0001698 0.0005447 0.00 0.0000000
common_brain_gene_psy 0.0003319 0.0011640 0.13 0.0331922
common_ses_gene_psy 0.0002193 0.0006192 0.09 0.0219318
common_brain_ses_psy 0.0213255 -0.0091058 8.30 2.1325547
common_brain_gene_ses 0.0004445 0.0011595 0.17 0.0444507
common_brain_gene_psy_ses 0.0028829 0.4766184 1.12 0.2882852 
common_analysis_gene_psy_brain_ses_followup[[2]]%>% 
    kableExtra::kbl(caption = "R^2 metrics for all models in followup") %>%
    kableExtra::kable_classic(full_width = F, 
                             html_font = "Cambria")
R^2 metrics for all models in followup
model_names <- … marginal_rsq conditional_rsq
gene 0.0065919 0.4961291
brain 0.1779084 0.5219121
psy 0.0855432 0.4922079
ses 0.1072818 0.4681534
brain_psy 0.2170084 0.5370952
gene_psy 0.0884150 0.5095280
brain_gene 0.1794846 0.5345675
brain_ses 0.2355775 0.5238731
psy_ses 0.1495132 0.4932299
gene_ses 0.1104969 0.4853407
brain_gene_psy 0.2180792 0.5487239
ses_gene_psy 0.1521103 0.5088457
brain_ses_psy 0.2555740 0.5394373
brain_gene_ses 0.2371042 0.5353645
brain_gene_psy_ses 0.2568146 0.5505212

4.5 Baseline percentage of rsquare plot

overrideTriple=TRUE

quad_venn_baseline_percent <- draw.quad.venn(area1 = baseline_gene_psy_brain_ses_vec_corrected_percent[2]+baseline_gene_psy_brain_ses_vec_corrected_percent[5]+baseline_gene_psy_brain_ses_vec_corrected_percent[7]+baseline_gene_psy_brain_ses_vec_corrected_percent[8]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  area2 = baseline_gene_psy_brain_ses_vec_corrected_percent[3]+baseline_gene_psy_brain_ses_vec_corrected_percent[5]+baseline_gene_psy_brain_ses_vec_corrected_percent[6]+baseline_gene_psy_brain_ses_vec_corrected_percent[9]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  area3 = baseline_gene_psy_brain_ses_vec_corrected_percent[1]+baseline_gene_psy_brain_ses_vec_corrected_percent[6]+baseline_gene_psy_brain_ses_vec_corrected_percent[7]+baseline_gene_psy_brain_ses_vec_corrected_percent[10]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  area4 =  baseline_gene_psy_brain_ses_vec_corrected_percent[4]+baseline_gene_psy_brain_ses_vec_corrected_percent[8]+baseline_gene_psy_brain_ses_vec_corrected_percent[9]+baseline_gene_psy_brain_ses_vec_corrected_percent[10]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n12 = baseline_gene_psy_brain_ses_vec_corrected_percent[5]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n13 = baseline_gene_psy_brain_ses_vec_corrected_percent[7]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n23 = baseline_gene_psy_brain_ses_vec_corrected_percent[6]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n14 = baseline_gene_psy_brain_ses_vec_corrected_percent[8]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n24 = baseline_gene_psy_brain_ses_vec_corrected_percent[9]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n34 = baseline_gene_psy_brain_ses_vec_corrected_percent[10]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n123 = baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n124 = baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n134 = baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n234 = baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n1234 = baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  category = c("Brain", "Mental Health", "Genes","Social Demographic Lifestyle"),
  fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.2, 0.2,0.1,0.1), # Modified
  cat.pos = c(1,1,1,1),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F,
  direct.area=TRUE)

grid.newpage()

#grid::grid.draw(quad_venn_baseline_percent)
#invisible(dev.off())

The plot with label.

#overrideTriple=TRUE

quad_venn_baseline_percent_label <- draw.quad.venn(area1 = baseline_gene_psy_brain_ses_vec_corrected_percent[2]+baseline_gene_psy_brain_ses_vec_corrected_percent[5]+baseline_gene_psy_brain_ses_vec_corrected_percent[7]+baseline_gene_psy_brain_ses_vec_corrected_percent[8]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  area2 = baseline_gene_psy_brain_ses_vec_corrected_percent[3]+baseline_gene_psy_brain_ses_vec_corrected_percent[5]+baseline_gene_psy_brain_ses_vec_corrected_percent[6]+baseline_gene_psy_brain_ses_vec_corrected_percent[9]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  area3 = baseline_gene_psy_brain_ses_vec_corrected_percent[1]+baseline_gene_psy_brain_ses_vec_corrected_percent[6]+baseline_gene_psy_brain_ses_vec_corrected_percent[7]+baseline_gene_psy_brain_ses_vec_corrected_percent[10]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  area4 =  baseline_gene_psy_brain_ses_vec_corrected_percent[4]+baseline_gene_psy_brain_ses_vec_corrected_percent[8]+baseline_gene_psy_brain_ses_vec_corrected_percent[9]+baseline_gene_psy_brain_ses_vec_corrected_percent[10]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n12 = baseline_gene_psy_brain_ses_vec_corrected_percent[5]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n13 = baseline_gene_psy_brain_ses_vec_corrected_percent[7]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n23 = baseline_gene_psy_brain_ses_vec_corrected_percent[6]+baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n14 = baseline_gene_psy_brain_ses_vec_corrected_percent[8]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n24 = baseline_gene_psy_brain_ses_vec_corrected_percent[9]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n34 = baseline_gene_psy_brain_ses_vec_corrected_percent[10]+baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n123 = baseline_gene_psy_brain_ses_vec_corrected_percent[11]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n124 = baseline_gene_psy_brain_ses_vec_corrected_percent[13]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n134 = baseline_gene_psy_brain_ses_vec_corrected_percent[14]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n234 = baseline_gene_psy_brain_ses_vec_corrected_percent[12]+baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  n1234 = baseline_gene_psy_brain_ses_vec_corrected_percent[15],
  category = c("Brain", "Mental Health", "Genes","Social Demographic Lifestyle"),
  fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
    filename = NULL,
  cex = 2, ## label font size
  cat.cex = 2.5,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.2, 0.2,0.1,0.1), # Modified
  cat.pos = c(1,1,1,1),# Modified
  print.mode="percent"
  )

grid.newpage()

#grid::grid.draw(quad_venn_baseline_percent_label)
#invisible(dev.off())

The raw r-squared plot.

overrideTriple=TRUE

quad_venn_baseline_raw <- draw.quad.venn(area1 = baseline_gene_psy_brain_ses_vec_corrected_raw[2]+baseline_gene_psy_brain_ses_vec_corrected_raw[5]+baseline_gene_psy_brain_ses_vec_corrected_raw[7]+baseline_gene_psy_brain_ses_vec_corrected_raw[8]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  area2 = baseline_gene_psy_brain_ses_vec_corrected_raw[3]+baseline_gene_psy_brain_ses_vec_corrected_raw[5]+baseline_gene_psy_brain_ses_vec_corrected_raw[6]+baseline_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  area3 = baseline_gene_psy_brain_ses_vec_corrected_raw[1]+baseline_gene_psy_brain_ses_vec_corrected_raw[6]+baseline_gene_psy_brain_ses_vec_corrected_raw[7]+baseline_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  area4 =  baseline_gene_psy_brain_ses_vec_corrected_raw[4]+baseline_gene_psy_brain_ses_vec_corrected_raw[8]+baseline_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n12 = baseline_gene_psy_brain_ses_vec_corrected_raw[5]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n13 = baseline_gene_psy_brain_ses_vec_corrected_raw[7]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n23 = baseline_gene_psy_brain_ses_vec_corrected_raw[6]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n14 = baseline_gene_psy_brain_ses_vec_corrected_raw[8]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n24 = baseline_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n34 = baseline_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n123 = baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n124 = baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n134 = baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n234 = baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  n1234 = baseline_gene_psy_brain_ses_vec_corrected_raw[15],
  category = c("Brain", "Mental Health", "Genes","Social Demographic Lifestyle"),
  fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.2, 0.2,0.1,0.1), # Modified
  cat.pos = c(1,1,1,1),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F,
  direct.area=TRUE)

grid.newpage()

#grid::grid.draw(quad_venn_baseline_raw)
#invisible(dev.off())

Raw r-square score with label

The following function is changed from the source code of the package;

The original code is in this page:

https://github.com/cran/VennDiagram/blob/master/R/draw.quad.venn.R

draw.quad.venn_round <- function(
    area1,
    area2,
    area3,
    area4,
    n12,
    n13,
    n14,
    n23,
    n24,
    n34,
    n123,
    n124,
    n134,
    n234,
    n1234,
    category = rep('', 4),
    lwd = rep(2, 4),
    lty = rep('solid', 4),
    col = rep('black', 4),
    fill = NULL,
    alpha = rep(0.5, 4),
    label.col = rep('black', 15),
    cex = rep(1, 15),
    fontface = rep('plain', 15),
    fontfamily = rep('serif', 15),
    cat.pos = c(-15, 15, 0, 0),
    cat.dist = c(0.22, 0.22, 0.11, 0.11),
    cat.col = rep('black', 4),
    cat.cex = rep(1, 4),
    cat.fontface = rep('plain', 4),
    cat.fontfamily = rep('serif', 4),
    cat.just = rep(list(c(0.5, 0.5)), 4),
    rotation.degree = 0,
    rotation.centre = c(0.5, 0.5),
    ind = TRUE,
    cex.prop=NULL,
    print.mode = 'raw',
    sigdigs=3,
    direct.area = FALSE,
    area.vector = 0,
    ...
    ) {

    #area1 > area2 > area3 > area4
    # check parameter lengths
    if (length(category) == 1) { cat <- rep(category, 4); }
    else if (length(category) != 4) { flog.error('Unexpected parameter length for "category"',name='VennDiagramLogger')
stop('Unexpected parameter length for "category"'); }

    if (length(lwd) == 1) { lwd <- rep(lwd, 4); }
    else if (length(lwd) != 4) { flog.error('Unexpected parameter length for "lwd"',name='VennDiagramLogger')
stop('Unexpected parameter length for "lwd"'); }

    if (length(lty) == 1) { lty <- rep(lty, 4); }
    else if (length(lty) != 4) { flog.error('Unexpected parameter length for "lty"',name='VennDiagramLogger')
stop('Unexpected parameter length for "lty"'); }

    if (length(col) == 1) { col <- rep(col, 4); }
    else if (length(col) != 4) { flog.error('Unexpected parameter length for "col"',name='VennDiagramLogger')
stop('Unexpected parameter length for "col"'); }

    if (length(label.col) == 1) { label.col <- rep(label.col, 15); }
    else if (length(label.col) != 15) { flog.error('Unexpected parameter length for "label.col"',name='VennDiagramLogger')
stop('Unexpected parameter length for "label.col"'); }

    if (length(cex) == 1) { cex <- rep(cex, 15); }
    else if (length(cex) != 15) { flog.error('Unexpected parameter length for "cex"',name='VennDiagramLogger')
stop('Unexpected parameter length for "cex"'); }

    if (length(fontface) == 1) { fontface <- rep(fontface, 15); }
    else if (length(fontface) != 15) { flog.error('Unexpected parameter length for "fontface"',name='VennDiagramLogger')
stop('Unexpected parameter length for "fontface"'); }

    if (length(fontfamily) == 1) { fontfamily <- rep(fontfamily, 15); }
    else if (length(fontfamily) != 15) { flog.error('Unexpected parameter length for "fontfamily"',name='VennDiagramLogger')
stop('Unexpected parameter length for "fontfamily"'); }

    if (length(fill) == 1) { fill <- rep(fill, 4); }
    else if (length(fill) != 4 & length(fill) != 0) { flog.error('Unexpected parameter length for "fill"',name='VennDiagramLogger')
stop('Unexpected parameter length for "fill"'); }

    if (length(alpha) == 1) { alpha <- rep(alpha, 4); }
    else if (length(alpha) != 4 & length(alpha) != 0) { flog.error('Unexpected parameter length for "alpha"',name='VennDiagramLogger')
stop('Unexpected parameter length for "alpha"'); }

    if (length(cat.pos) == 1) { cat.pos <- rep(cat.pos, 4); }
    else if (length(cat.pos) != 4) { flog.error('Unexpected parameter length for "cat.pos"',name='VennDiagramLogger')
stop('Unexpected parameter length for "cat.pos"'); }

    if (length(cat.dist) == 1) { cat.dist <- rep(cat.dist, 4); }
    else if (length(cat.dist) != 4) { flog.error('Unexpected parameter length for "cat.dist"',name='VennDiagramLogger')
stop('Unexpected parameter length for "cat.dist"'); }

    if (length(cat.col) == 1) { cat.col <- rep(cat.col, 4); }
    else if (length(cat.col) != 4) { flog.error('Unexpected parameter length for "cat.col"',name='VennDiagramLogger')
stop('Unexpected parameter length for "cat.col"'); }

    if (length(cat.cex) == 1) { cat.cex <- rep(cat.cex, 4); }
    else if (length(cat.cex) != 4) { flog.error('Unexpected parameter length for "cat.cex"',name='VennDiagramLogger')
stop('Unexpected parameter length for "cat.cex"'); }

    if (length(cat.fontface) == 1) { cat.fontface <- rep(cat.fontface, 4); }
    else if (length(cat.fontface) != 4) { flog.error('Unexpected parameter length for "cat.fontface"',name='VennDiagramLogger')
stop('Unexpected parameter length for "cat.fontface"'); }

    if (length(cat.fontfamily) == 1) { cat.fontfamily <- rep(cat.fontfamily, 4); }
    else if (length(cat.fontfamily) != 4) { flog.error('Unexpected parameter length for "cat.fontfamily"',name='VennDiagramLogger')
stop('Unexpected parameter length for "cat.fontfamily"'); }

    if (!(is.list(cat.just) && length(cat.just) == 4 && length(cat.just[[1]]) == 2 && length(cat.just[[2]]) == 2 && length(cat.just[[3]]) == 2 && length(cat.just[[4]]) == 2)) { flog.error('Unexpected parameter format for "cat.just"',name='VennDiagramLogger')
stop('Unexpected parameter format for "cat.just"'); }
    cat.pos <- cat.pos + rotation.degree;
    
    if(direct.area){
        areas <- area.vector;
        #create the variables and assign their values from the area vector
        for(i in 1:15)
        {
            assign(paste('a',i,sep=''),area.vector[i]);
        }
    }
    else {
        # generate partial areas from given arguments
        a6  <- round(n1234,2);
        a12 <- round(n123 - a6,2);
        a11 <- round(n124 - a6,2);
        a5  <- round(n134 - a6,2);
        a7  <- round(n234 - a6,2);
        a15 <- round(n12 - a6 - a11 - a12,2);
        a4  <- round(n13 - a6 - a5 - a12,2);
        a10 <- round(n14 - a6 - a5 - a11,2);
        a13 <- round(n23 - a6 - a7 - a12,2);
        a8  <- round(n24 - a6 - a7 - a11,2);
        a2  <- round(n34 - a6 - a5 - a7,2);
        a9  <- round(area1 - a4 - a5 - a6 - a10 - a11 - a12 - a15,2);
        a14 <- round(area2 - a6 - a7 - a8 - a11 - a12 - a13 - a15,2);
        a1  <- round(area3 - a2 - a4 - a5 - a6 - a7 - a12 - a13,2);
        a3  <- round(area4 - a2 - a5 - a6 - a7 - a8 - a10 - a11,2);

        # check plausibility and 0 partial areas
        areas <- c(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15);
    }
    areas.error <- c(
        'a1  <- area3 - a2 - a4 - a5 - a6 - a7 - a12 - a13',
        'a2  <- n34 - a6 - a5 - a7',
        'a3  <- area4 - a2 - a5 - a6 - a7 - a8 - a10 - a11',
        'a4  <- n13 - a6 - a5 - a12',
        'a5  <- n134 - a6',
        'a6  <- n1234',
        'a7  <- n234 - a6',
        'a8  <- n24 - a6 - a7 - a11',
        'a9  <- area1 - a4 - a5 - a6 - a10 - a11 - a12 - a15',
        'a10 <- n14 - a6 - a5 - a11',
        'a11 <- n124 - a6',
        'a12 <- n123 - a6',
        'a15 <- n12 - a6 - a11 - a12',
        'a13 <- n23 - a6 - a7 - a12',
        'a14 <- area2 - a6 - a7 - a8 - a11 - a12 - a13 - a15'
        );
    for (i in 1:length(areas)) {
        if (areas[i] < 0) {
            flog.error(paste('Impossible:', areas.error[i], 'produces negative area'),name='VennDiagramLogger')
stop(paste('Impossible:', areas.error[i], 'produces negative area'));
            }
        }

        ## rescaling area labels to be proportional to area
        if(length(cex.prop) > 0){

            if(length(cex.prop) != 1) {
        flog.error('Value passed to cex.prop is not length 1',name='VennDiagramLogger')
        stop('Value passed to cex.prop is not length 1')
        }

            ## figure out what function to use
            func = cex.prop
            if (!is(cex.prop, 'function')) {
                if(cex.prop == 'lin'){
                    func = function(x) x
                }
                else if(cex.prop == 'log10'){
                    func = log10
                }
                else flog.error(paste0('Unknown value passed to cex.prop: ', cex.prop),name='VennDiagramLogger')
stop(paste0('Unknown value passed to cex.prop: ', cex.prop))
            }

            ## rescale areas
            maxArea = max(areas)            
            for(i in 1:length(areas)){                
                cex[i] = cex[i] * func(areas[i]) / func(maxArea)
                if(cex[i] <= 0) stop(paste0('Error in rescaling of area labels: the label of area ',
                          i, ' is less than or equal to zero'))
            }
        }

    # initialize gList to hold all Grobs generated
    grob.list <- gList();

    # plot the ellipses of the Venn diagram
    ellipse.positions <- matrix(
        nrow = 4,
        ncol = 7
        );
    colnames(ellipse.positions) <- c('x', 'y', 'a', 'b', 'rotation', 'fill.mapping', 'line.mapping');

    ellipse.positions[1,] <- c(0.65, 0.47, 0.35, 0.20,  45, 2, 2);
    ellipse.positions[2,] <- c(0.35, 0.47, 0.35, 0.20, 135, 1, 1);
    ellipse.positions[3,] <- c(0.50, 0.57, 0.33, 0.15,  45, 4, 4);
    ellipse.positions[4,] <- c(0.50, 0.57, 0.35, 0.15, 135, 3, 3);

    # draw the ellipses themselves
    for (i in 1:4) {
        grob.list <- gList(
            grob.list,
            VennDiagram::ellipse(
                x = ellipse.positions[i,'x'],
                y = ellipse.positions[i,'y'],
                a = ellipse.positions[i,'a'],
                b = ellipse.positions[i,'b'],
                rotation = ellipse.positions[i, 'rotation'],
                gp = gpar(
                    lty = 0,
                    fill = fill[ellipse.positions[i,'fill.mapping']],
                    alpha = alpha[ellipse.positions[i,'fill.mapping']]
                    )
                )
            );
        }

    # draw the ellipse borders
    for (i in 1:4) {
        grob.list <- gList(
            grob.list,
            ellipse(
                x = ellipse.positions[i,'x'],
                y = ellipse.positions[i,'y'],
                a = ellipse.positions[i,'a'],
                b = ellipse.positions[i,'b'],
                rotation = ellipse.positions[i, 'rotation'],
                gp = gpar(
                    lwd = lwd[ellipse.positions[i,'line.mapping']],
                    lty = lty[ellipse.positions[i,'line.mapping']],
                    col = col[ellipse.positions[i,'line.mapping']],
                    fill = 'transparent'
                    )
                )
            );
        }

    # create the labels
    label.matrix <- matrix(
        nrow = 15,
        ncol = 3
        );
    colnames(label.matrix) <- c('label', 'x', 'y');

    label.matrix[ 1,] <- c(a1,  0.350, 0.77);
    label.matrix[ 2,] <- c(a2,  0.500, 0.69);
    label.matrix[ 3,] <- c(a3,  0.650, 0.77);
    label.matrix[ 4,] <- c(a4,  0.310, 0.67);
    label.matrix[ 5,] <- c(a5,  0.400, 0.58);
    label.matrix[ 6,] <- c(a6,  0.500, 0.47);
    label.matrix[ 7,] <- c(a7,  0.600, 0.58);
    label.matrix[ 8,] <- c(a8,  0.690, 0.67);
    label.matrix[ 9,] <- c(a9,  0.180, 0.58);
    label.matrix[10,] <- c(a10, 0.320, 0.42);
    label.matrix[11,] <- c(a11, 0.425, 0.38);
    label.matrix[12,] <- c(a12, 0.575, 0.38);
    label.matrix[13,] <- c(a13, 0.680, 0.42);
    label.matrix[14,] <- c(a14, 0.820, 0.58);
    label.matrix[15,] <- c(a15, 0.500, 0.28);

    processedLabels <- rep('',length(label.matrix[,'label']));
    if(print.mode[1] == 'percent'){
            processedLabels <- paste(signif(label.matrix[,'label']/sum(label.matrix[,'label'])*100,digits=sigdigs),'%',sep='');
            if(isTRUE(print.mode[2] == 'raw'))
            {
                processedLabels <- paste(processedLabels,'\n(',label.matrix[,'label'],')',sep='');
            }
        }
    if(print.mode[1] == 'raw'){
            processedLabels <- label.matrix[,'label'];
            if(isTRUE(print.mode[2] == 'percent'))
            {
                processedLabels <- paste(processedLabels,'\n(',paste(signif(label.matrix[,'label']/sum(label.matrix[,'label'])*100,digits=sigdigs),'%)',sep=''),sep='');
            }
        }
    
    
    for (i in 1:nrow(label.matrix)) {
        grob.list <- gList(
            grob.list,
            textGrob(
                label = processedLabels[i],
                x = label.matrix[i,'x'],
                y = label.matrix[i,'y'],
                gp = gpar(
                    col = label.col[i],
                    cex = cex[i],
                    fontface = fontface[i],
                    fontfamily = fontfamily[i]
                    )
                )
            );
        }
        

    # find the location and plot all the category names
    cat.pos.x <- c(0.18, 0.82, 0.35, 0.65);
    cat.pos.y <- c(0.58, 0.58, 0.77, 0.77);

    for (i in 1:4) {

        # work out location of the category label
        this.cat.pos <- find.cat.pos(
            x = cat.pos.x[i],
            y = cat.pos.y[i],
            pos = cat.pos[i],
            dist = cat.dist[i]
            );

        # then print it
        grob.list <- gList(
            grob.list,
            textGrob(
                label = category[i],
                x = this.cat.pos$x,
                y = this.cat.pos$y,
                just = cat.just[[i]],
                gp = gpar(
                    col = cat.col[i],
                    cex = cat.cex[i],
                    fontface = cat.fontface[i],
                    fontfamily = cat.fontfamily[i]
                    )
                )
            );
        }

    # adjust grob.list to fit and return grob.list
    grob.list <- VennDiagram::adjust.venn(VennDiagram::rotate.venn.degrees(grob.list, rotation.degree, rotation.centre[1], rotation.centre[2]), ...);

    # draw diagram before returning gList is specified by user
    if (ind) { grid.draw(grob.list); }
    return(grob.list);
    }
#overrideTriple=TRUE
quad_venn_baseline_raw_label <- draw.quad.venn_round(area1 = baseline_gene_psy_brain_ses_vec_corrected_raw[2]+baseline_gene_psy_brain_ses_vec_corrected_raw[5]+baseline_gene_psy_brain_ses_vec_corrected_raw[7]+baseline_gene_psy_brain_ses_vec_corrected_raw[8]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
area2 = baseline_gene_psy_brain_ses_vec_corrected_raw[3]+baseline_gene_psy_brain_ses_vec_corrected_raw[5]+baseline_gene_psy_brain_ses_vec_corrected_raw[6]+baseline_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
area3 = baseline_gene_psy_brain_ses_vec_corrected_raw[1]+baseline_gene_psy_brain_ses_vec_corrected_raw[6]+baseline_gene_psy_brain_ses_vec_corrected_raw[7]+baseline_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
area4 = baseline_gene_psy_brain_ses_vec_corrected_raw[4]+baseline_gene_psy_brain_ses_vec_corrected_raw[8]+baseline_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n12 = baseline_gene_psy_brain_ses_vec_corrected_raw[5]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n13 = baseline_gene_psy_brain_ses_vec_corrected_raw[7]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n23 = baseline_gene_psy_brain_ses_vec_corrected_raw[6]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n14 = baseline_gene_psy_brain_ses_vec_corrected_raw[8]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n24 = baseline_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n34 = baseline_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n123 =baseline_gene_psy_brain_ses_vec_corrected_raw[11]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n124 = baseline_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n134 = baseline_gene_psy_brain_ses_vec_corrected_raw[14]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n234 = baseline_gene_psy_brain_ses_vec_corrected_raw[12]+baseline_gene_psy_brain_ses_vec_corrected_raw[15],
n1234 = baseline_gene_psy_brain_ses_vec_corrected_raw[15],
category = c("Brain", "Mental \nHealth", "Genes","Socio-Demo \nLifestyle Dev"),
fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
lty = "dashed",
cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
filename = NULL,
cex = 2, ## label font size
cat.cex = 2,### caption font size
lwd = 2,
cat.fontface = "bold",
cat.dist = c(0.2, 0.25,0.1,0.13), # Modified
cat.pos = c(-5,10,1,-13),# Modified
#print.mode="percent"
)

grid.newpage()

#grid::grid.draw(quad_venn_baseline_raw_label)
#invisible(dev.off())

4.6 followup percentage of rsquare plot

overrideTriple=TRUE

quad_venn_followup_percent <- draw.quad.venn(area1 = followup_gene_psy_brain_ses_vec_corrected_percentage[2]+followup_gene_psy_brain_ses_vec_corrected_percentage[5]+followup_gene_psy_brain_ses_vec_corrected_percentage[7]+followup_gene_psy_brain_ses_vec_corrected_percentage[8]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  area2 = followup_gene_psy_brain_ses_vec_corrected_percentage[3]+followup_gene_psy_brain_ses_vec_corrected_percentage[5]+followup_gene_psy_brain_ses_vec_corrected_percentage[6]+followup_gene_psy_brain_ses_vec_corrected_percentage[9]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  area3 = followup_gene_psy_brain_ses_vec_corrected_percentage[1]+followup_gene_psy_brain_ses_vec_corrected_percentage[6]+followup_gene_psy_brain_ses_vec_corrected_percentage[7]+followup_gene_psy_brain_ses_vec_corrected_percentage[10]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  area4 =  followup_gene_psy_brain_ses_vec_corrected_percentage[4]+followup_gene_psy_brain_ses_vec_corrected_percentage[8]+followup_gene_psy_brain_ses_vec_corrected_percentage[9]+followup_gene_psy_brain_ses_vec_corrected_percentage[10]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n12 = followup_gene_psy_brain_ses_vec_corrected_percentage[5]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n13 = followup_gene_psy_brain_ses_vec_corrected_percentage[7]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n23 = followup_gene_psy_brain_ses_vec_corrected_percentage[6]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n14 = followup_gene_psy_brain_ses_vec_corrected_percentage[8]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n24 = followup_gene_psy_brain_ses_vec_corrected_percentage[9]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n34 = followup_gene_psy_brain_ses_vec_corrected_percentage[10]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n123 = followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n124 = followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n134 = followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n234 = followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n1234 = followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  category = c("Brain", "Mental Health", "Genes","Social Demographic Lifestyle"),
  fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.2, 0.2,0.1,0.1), # Modified
  cat.pos = c(1,1,1,1),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F,
  direct.area=TRUE)

grid.newpage()

#grid::grid.draw(quad_venn_followup_percent)
#invisible(dev.off())
#overrideTriple=TRUE

quad_venn_followup_percent_label <- draw.quad.venn(area1 = followup_gene_psy_brain_ses_vec_corrected_percentage[2]+followup_gene_psy_brain_ses_vec_corrected_percentage[5]+followup_gene_psy_brain_ses_vec_corrected_percentage[7]+followup_gene_psy_brain_ses_vec_corrected_percentage[8]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  area2 = followup_gene_psy_brain_ses_vec_corrected_percentage[3]+followup_gene_psy_brain_ses_vec_corrected_percentage[5]+followup_gene_psy_brain_ses_vec_corrected_percentage[6]+followup_gene_psy_brain_ses_vec_corrected_percentage[9]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  area3 = followup_gene_psy_brain_ses_vec_corrected_percentage[1]+followup_gene_psy_brain_ses_vec_corrected_percentage[6]+followup_gene_psy_brain_ses_vec_corrected_percentage[7]+followup_gene_psy_brain_ses_vec_corrected_percentage[10]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  area4 =  followup_gene_psy_brain_ses_vec_corrected_percentage[4]+followup_gene_psy_brain_ses_vec_corrected_percentage[8]+followup_gene_psy_brain_ses_vec_corrected_percentage[9]+followup_gene_psy_brain_ses_vec_corrected_percentage[10]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n12 = followup_gene_psy_brain_ses_vec_corrected_percentage[5]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n13 = followup_gene_psy_brain_ses_vec_corrected_percentage[7]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n23 = followup_gene_psy_brain_ses_vec_corrected_percentage[6]+followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n14 = followup_gene_psy_brain_ses_vec_corrected_percentage[8]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n24 = followup_gene_psy_brain_ses_vec_corrected_percentage[9]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n34 = followup_gene_psy_brain_ses_vec_corrected_percentage[10]+followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n123 = followup_gene_psy_brain_ses_vec_corrected_percentage[11]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n124 = followup_gene_psy_brain_ses_vec_corrected_percentage[13]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n134 = followup_gene_psy_brain_ses_vec_corrected_percentage[14]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n234 = followup_gene_psy_brain_ses_vec_corrected_percentage[12]+followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  n1234 = followup_gene_psy_brain_ses_vec_corrected_percentage[15],
  category = c("Brain", "Mental Health", "Genes","Social Demographic Lifestyle"),
  fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
    filename = NULL,
  cex = 2, ## label font size
  cat.cex = 2.5,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.2, 0.2,0.1,0.1), # Modified
  cat.pos = c(1,1,1,1),# Modified
  print.mode="percent"
  )

grid.newpage()

#grid::grid.draw(quad_venn_followup_percent_label)
#invisible(dev.off())

The raw R-squared plot

overrideTriple=TRUE

quad_venn_followup_raw <- draw.quad.venn(area1 = followup_gene_psy_brain_ses_vec_corrected_raw[2]+followup_gene_psy_brain_ses_vec_corrected_raw[5]+followup_gene_psy_brain_ses_vec_corrected_raw[7]+followup_gene_psy_brain_ses_vec_corrected_raw[8]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  area2 = followup_gene_psy_brain_ses_vec_corrected_raw[3]+followup_gene_psy_brain_ses_vec_corrected_raw[5]+followup_gene_psy_brain_ses_vec_corrected_raw[6]+followup_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  area3 = followup_gene_psy_brain_ses_vec_corrected_raw[1]+followup_gene_psy_brain_ses_vec_corrected_raw[6]+followup_gene_psy_brain_ses_vec_corrected_raw[7]+followup_gene_psy_brain_ses_vec_corrected_raw[10]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  area4 =  followup_gene_psy_brain_ses_vec_corrected_raw[4]+followup_gene_psy_brain_ses_vec_corrected_raw[8]+followup_gene_psy_brain_ses_vec_corrected_raw[9]+followup_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n12 = followup_gene_psy_brain_ses_vec_corrected_raw[5]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n13 = followup_gene_psy_brain_ses_vec_corrected_raw[7]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n23 = followup_gene_psy_brain_ses_vec_corrected_raw[6]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n14 = followup_gene_psy_brain_ses_vec_corrected_raw[8]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n24 = followup_gene_psy_brain_ses_vec_corrected_raw[9]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n34 = followup_gene_psy_brain_ses_vec_corrected_raw[10]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n123 = followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n124 = followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n134 = followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n234 = followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n1234 = followup_gene_psy_brain_ses_vec_corrected_raw[15],
  category = c("Brain", "Mental \nHealth", "Genes","Socio-Demo \nLifestyle Dev"),
  fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  filename = NULL,
  cex = 0.001, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
  cat.dist = c(0.2, 0.2,0.1,0.1), # Modified
  cat.pos = c(1,1,1,1),# Modified
  print.mode="percent",
  euler.d=F,
  scaled=F,
  reverse = F,
  direct.area=TRUE)

grid.newpage()

#grid::grid.draw(quad_venn_followup_raw)
#invisible(dev.off())
#overrideTriple=TRUE

quad_venn_followup_raw_label <- draw.quad.venn_round(area1 = followup_gene_psy_brain_ses_vec_corrected_raw[2]+followup_gene_psy_brain_ses_vec_corrected_raw[5]+followup_gene_psy_brain_ses_vec_corrected_raw[7]+followup_gene_psy_brain_ses_vec_corrected_raw[8]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  area2 = followup_gene_psy_brain_ses_vec_corrected_raw[3]+followup_gene_psy_brain_ses_vec_corrected_raw[5]+followup_gene_psy_brain_ses_vec_corrected_raw[6]+followup_gene_psy_brain_ses_vec_corrected_raw[9]+baseline_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  area3 = followup_gene_psy_brain_ses_vec_corrected_raw[1]+followup_gene_psy_brain_ses_vec_corrected_raw[6]+followup_gene_psy_brain_ses_vec_corrected_raw[7]+followup_gene_psy_brain_ses_vec_corrected_raw[10]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  area4 =  followup_gene_psy_brain_ses_vec_corrected_raw[4]+followup_gene_psy_brain_ses_vec_corrected_raw[8]+followup_gene_psy_brain_ses_vec_corrected_raw[9]+followup_gene_psy_brain_ses_vec_corrected_raw[10]+baseline_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+baseline_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n12 = followup_gene_psy_brain_ses_vec_corrected_raw[5]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n13 = followup_gene_psy_brain_ses_vec_corrected_raw[7]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n23 = followup_gene_psy_brain_ses_vec_corrected_raw[6]+followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n14 = followup_gene_psy_brain_ses_vec_corrected_raw[8]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n24 = followup_gene_psy_brain_ses_vec_corrected_raw[9]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n34 = followup_gene_psy_brain_ses_vec_corrected_raw[10]+followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n123 = followup_gene_psy_brain_ses_vec_corrected_raw[11]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n124 = followup_gene_psy_brain_ses_vec_corrected_raw[13]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n134 = followup_gene_psy_brain_ses_vec_corrected_raw[14]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n234 = followup_gene_psy_brain_ses_vec_corrected_raw[12]+followup_gene_psy_brain_ses_vec_corrected_raw[15],
  n1234 = followup_gene_psy_brain_ses_vec_corrected_raw[15],
  category = c("Brain", "Mental \nHealth", "Genes","Socio-Demo \nLifestyle Dev"),
  fill = c("#009E73", "#D55E00", "#CC79A7","#999999"),
  lty = "dashed",
  cat.col = c("#009E73", "#D55E00", "#CC79A7","#999999"),
    filename = NULL,
  cex = 2, ## label font size
  cat.cex = 2,### caption font size
  lwd = 2,
  cat.fontface = "bold",
   cat.dist = c(0.2, 0.25,0.1,0.13), # Modified
  cat.pos = c(-5,10,1,-13),# Modified
  #print.mode="percent"
  )

grid.newpage()

#grid::grid.draw(quad_venn_followup_raw_label)
#invisible(dev.off())

5 draw the label of the venn diagrams

Just draw the title of the venn diagram. Only the equation part is extracted.

ggplot()+
  labs(title =  "<span style='font-size:15pt'>Cognitive Abilities ~ 
    <span style='color:#009E73;'>Brain</span> +
   <span style='color:#CC79A7;'>Genes</span> +
   <span style='color:#999999;'>Socio-Demo Lifestyle Dev</span> +
   <span style='color:#D55E00;'>Mental Health</span>
    </span>",
    x = NULL, y = NULL
  )+
theme(legend.position = "none",
      plot.title = element_markdown(lineheight = 1,size = 15))

ggplot()+
  labs(title =  "<span style='font-size:15pt'>Cognitive Abilities ~ 
    <span style='color:#009E73;'>Brain</span> +
   <span style='color:#D55E00;'>Mental Health</span>
    </span>",
    x = NULL, y = NULL
  )+
theme(legend.position = "none",
      plot.title = element_markdown(lineheight = 1,size = 15))

ggplot()+
  labs(title =  "<span style='font-size:15pt'>Cognitive Abilities ~ 
   <span style='color:#999999;'>Socio-Demo Lifestyle Dev</span> +
   <span style='color:#D55E00;'>Mental Health</span>
    </span>",
    x = NULL, y = NULL
  )+
theme(legend.position = "none",
      plot.title = element_markdown(lineheight = 1,size = 15))

ggplot()+
  labs(title =  "<span style='font-size:15pt'>Cognitive Abilities ~ 
    <span style='color:#D55E00;'>Mental Health</span> +
   <span style='color:#CC79A7;'>Genes</span> 
    </span>",
    x = NULL, y = NULL
  )+
theme(legend.position = "none",
      plot.title = element_markdown(lineheight = 1,size = 15))